Question: Performance analysis and EM design of a low RCS microstrip patch array antenna.

This paper introduces a novel frequency selective radome that exhibits wideband absorbing properties, serving as a passband filter within a specific frequency range and as an absorber beyond this range. The passband functionality is achieved through the use of a compact metallic Frequency Selective Surface (FSS) composed of interdigitated Jerusalem cross elements, which exhibit a broad rejection band. This metallic FSS is incorporated as the ground plane of a thin wideband absorber, which is based on resistive high-impedance surfaces within the total reflection band. The outer absorber effectively reduces the radar cross section (RCS) of the antenna system when illuminated by signals outside the radome's transmission band. To minimize losses within the radome's transmitting band, the resistive FSS that constitutes the absorber is meticulously designed. The composite structure's performance is rigorously analyzed using an efficient equivalent circuit approach and full-wave numerical simulations, ensuring its effectiveness in both filtering and absorbing functions.

**Question**: A Novel Approach for the Detection of Malicious URLs Using Machine Learning Techniques

**Documents**: An efficient web-based malware detection system using machine learning techniques
Web-based malware detection is a challenging task due to the large volume of web traffic and the sophisticated methods used by cybercriminals to evade detection. In this paper, we propose an efficient web-based malware detection system that uses machine learning techniques to detect malicious URLs. Our system is based on a two-stage classification process. In the first stage, we use a set of features extracted from the URLs to classify them as either benign or suspicious. In the second stage, we apply a more detailed analysis to the suspicious URLs to determine if they are indeed malicious. We use a variety of machine learning algorithms, including support vector machines, decision trees, and random forests, to build our classifiers. We evaluate the performance of our system using a large dataset of known malicious and benign URLs. The experimental results show that our system is able to detect a high percentage of malicious URLs while maintaining a low false positive rate.

**Similar Documents**: This paper presents a novel approach for detecting web-based malware using advanced machine learning techniques, addressing the significant challenge posed by the vast volume of web traffic and the sophisticated tactics employed by cybercriminals to avoid detection. Our proposed system employs a two-tier classification methodology to identify malicious URLs. Initially, a set of features extracted from the URLs is used to categorize them as benign or suspicious. Subsequently, a more comprehensive analysis is conducted on the suspicious URLs to ascertain their malicious nature. To construct our classifiers,

Automotive radar systems utilizing the millimeterwave band have been developed 2) since they can detect targets even in bad weather. In the automotive radar systems, linear polarization inclined 45 degrees is utilized in order to avoid mutual radio interference between them. The antenna for the automotive radar systems is required to have high efficiency, which means high gain against for an aperture area determined by the size of a radar sensor, in order to detect the targets at a long distance. In addition, low profile is required not to spoil the appearance of a car. Ease of manufacturing is also one of the significant factors in order to install the automotive radar systems in popular cars. As candidate antennas for the automotive radar systems, a slotted waveguide array antenna and a triplate-type array antenna have been studied. These antennas have low profile and high efficiency 7

In this letter, the log-periodic square fractal geometry is presented for the design of a miniaturized patch antenna for the ultra-wideband (UWB) services (3.1-10.6 GHz). A miniaturization factor of 23% is achieved with a constant and stable gain in the desired band. The radiation pattern is broadside, which finds suitable applications in the UWB radars and medical imaging. Furthermore, the time-domain performance of the proposed antenna is investigated. A prototype model of the proposed antenna is fabricated and measured as a proof of concept.

In this paper, corporate-feed circularly polarized microstrip array antennas are studied. The antenna element is a series-feed slot-coupled structure. Series feeding causes sequential rotation effect at the element level. Antenna elements are then used to form the subarray by applying sequential rotation to their feeding. Arrays having 4, 16, and 64 elements were made. The maximum achieved gains are 15.3, 21, and 25.4 dBic, respectively. All arrays have less than 15 dB return loss and 3 dB axial ratio from 10 to 13 GHz. The patterns are all quite symmetrical.

This paper discusses a fabrication approach and experimental validation of a very large, planar active electronically scanned array (AESA). The planar AESA architecture employs a monolithic printed circuit board (PCB) with 768 active antenna elements at X-Band. Manufacturing physically large arrays with high element counts is discussed in relation to construction, assembly and yield considerations. Measured active array patterns of the ESA are also presented.

In this paper, a microstrip-line fed printed monopole rectangular microstrip patch antenna with wide bandwidth having frequency band-notch function for wireless communication have been proposed. Bandwidth enhancement is obtained by rounding the corners of rectangular patch and modifying the ground plane. By cutting rectangular slot on the radiating element, the frequency band-notch characteristic is obtained. FR-4 substrate having dielectric constant of 4.4 and thickness of 1.6 mm is used to simulate printed rectangular monopole antenna (PRMA) having an overall size of substrate 55 × 45 mm2. The designed antenna is simulated using CAD FEKO simulation software. The proposed antenna has impedance bandwidth of 6.61 GHz (2.11 GHz to 8.72 GHz) with a rejection band from 3.03 GHz to 3.48 GHz. The observed gain of proposed antenna is 2.42 dBi. The proposed microstrip patch antenna is fabricated, tested and measured result is presented in this paper.

A sidelobe suppression method for a series slot array antenna which radiates 45° -inclined linear polarization is proposed. Axial displacements are employed to create arbitrary excitation coefficients for individual centered-inclined radiating slots along the center line of a broad wall. To verify the proposed design method, we design two types of center-fed linear slot array antennas with a Dolph-Chebyshev distribution for -20 dB and -26 dB sidelobe levels (SLLs) in the Ka band. Furthermore, a cross-validation process involving an equivalent circuit model analysis and electromagnetic full-wave simulation using CST MWS is utilized. The entire structure of the proposed series slot array antenna is fabricated on printed circuit boards (PCBs), including drilling and chemical etching, to secure advantages of miniaturization and cost reduction. The measured realized gains are 15.17 and 15.95 dBi and SLLs are -18.7 and -22.5 dB respectively for two types of fabricated antennas. It demonstrates the validity of the proposed sidelobe suppression method.

To meet the demands of high speed required by mobile communication of past generations ,one solution is to increase the number of antennas to the show and the reception of the wireless link this is called MIMO (Multiple input ,Multiple output )technology .however ,the integration of multiple antennas on the same PCB is delicate because of the small volume that require some applications and electromagnetic antenna between the coupling ,phenomena that we cannot neglect them .indeed a strong isolation between them has been reached to reduce fading of the signal caused by the electromagnetic antenna reached to reduce fading of the signal caused by the electromagnetic coupling and maximize the overall gain .in this article we are interested then integration on the same printed circuit of eight antennas MIMO are not operation in the same frequency band .the first antenna of this last work at 2.4GHz .other antennas have resonance frequency folling each with 20MHz offset this device is characterized by its original form that keeps is highly isolated antennas from the point of view electromagnetic coupling . INDEX TERMS MIMO, Technology Micro-strip, Microwave, Network Antenna

This paper propounds a new flexible material, which has been designed by employing flexible Poly Vinyl Chloride (PVC) having dielectric constant εr= 2.7 as substrate. The projected antenna design states that a circular shaped copper patch with flag shaped slot on it has been deployed over the hexagonal PVC substrate of thickness 1 mm. Then ground plane is partially minimized and slotted to revamp the antenna performance. The proposed antenna operates within frequency range of 7.2032GHz to 7.9035GHz while resonating at 7.55GHz. The proposed antenna design has minimal return loss of −55.226dB with the impedance bandwidth of 700 MHz, gain of 4.379dB and directivity of 4.111dBi. The antenna has VSWR less than maximum acceptable value of 2. The projected antenna design can be suitably employed for UWB, radio-determination applications (7.235GHz to 7.25GHz), naval, defence systems (7.25GHz – 7.3GHz) and weather satellite applications (7.75GHz to 7.90GHz). The antenna has been designed in CST Microwave Studio 2014. The proposed antenna has been fabricated and tested using E5071C Network Analyser and anechoic chamber. It has been observed that the stimulated results closely match with the experimental results.

The design of a new microstrip-line-fed wideband circularly polarized (CP) annular-ring slot antenna (ARSA) is proposed. Compared with existing ring slot antennas, the ARSAs designed here possess much larger CP bandwidths. The main features of the proposed design include a wider ring slot, a pair of grounded hat-shaped patches, and a deformed bent feeding microstrip line. The ARSAs designed using FR4 substrates in the L and S bands have 3-dB axial-ratio bandwidths (ARBWs) of as large as 46% and 56%, respectively, whereas the one using an RT5880 substrate in the L band, 65%. In these 3-dB axial-ratio bands, impedance matching with VSWR ≤ 2 is also achieved.

In this article, the authors use a 7 x 7 rectangular-ring unit metasurface and a single-feed, CP, rectangular, slotted patch antenna to enhance bandwidth. The antenna consists of a rectangular, slotted patch radiator, a metasurface with an array of rectangular-ring cells, and a coaxial feed. Using the metasurface results in a wide CP bandwidth sevenfold the size of a conventional antenna. The measured 3-dB-AR bandwidth of an antenna prototype printed onto an FR4 substrate is 28.3% (3.62 GHz-4.75 GHz) with 2:1-voltage standing wave ratio (VSWR) bandwidth of 36%. A 6.0-dBic, boresight gain with a variation of 1.5 dB is achieved across the frequency band of 3.35 GHz-4.75 GHz, with the maximum gain of 7.4 dBic at 4.1 GHz. We measure and compare the antenna prototype with a simulation using CST Microwave Studio. Parametric studies aid in understanding the proposed antenna's operation mechanism.


Question: Performance analysis and EM design of a low RCS microstrip patch array antenna.

This paper introduces a novel Polarization Reconfigurable Metasurface Antenna (PRMSA) that employs metasurface (MS) technology for enhanced functionality. The proposed PRMSA features a compact and low-profile design, comprising a planar MS positioned directly above a circular slot antenna, both sharing the same circular shape with a diameter of 78 mm (0.9 λ0). By rotating the MS relative to the slot antenna around the center, the PRMSA can switch between linear polarization and left-hand or right-hand circular polarizations, offering versatile polarization reconfigurability. An equivalent circuit model is utilized to elucidate the reconfigurability mechanism of the antenna. The PRMSA is meticulously designed and optimized to operate around 3.5 GHz through computer simulations. To validate the simulation outcomes, a prototype is fabricated and tested. The performance evaluation covers polarization reconfigurability, axial-ratio bandwidth, impedance bandwidth, realized boresight gain, and radiation pattern. Experimental results confirm that in circular polarization modes, the PRMSA exhibits an operational bandwidth of 3.3-3.7 GHz (11.4% fractional bandwidth), a boresight gain exceeding 5 dBi, and polarization isolation of more than 15 dB. In linear polarization, the antenna achieves a gain greater than 7.5 dBi with cross-polarization isolation surpassing 50 dB.

A brief review of metamaterials and their applications to antenna systems is given. Artificial magnetic conductors and electrically small radiating and scattering systems are emphasized. Single negative, double negative, and zero-index metamaterial systems are discussed as a means to manipulate their size, efficiency, bandwidth, and directivity characteristics. key words: metamaterials, electrically small antennas, complex media, artificial magnetic conductors

This paper presents the design of microstrip rectangular patch antenna with center frequency at 2.5GHz for WiMAX application. The array of four by one (4x1) patch array microstrip rectangular antenna with microstrip line feeding based on quarter wave impedance matching technique was designed and simulated using Computer Simulation Tool (CST) Microwave Environment software. The performance of the designed antenna was than compared with the single patch rectangle antenna in term of return loss, Voltage Standing Wave Ratio (VSWR), bandwidth, directivity, radiation pattern and gain. The array antenna was then fabricated on the substrate type FR-4 with dielectric constant of 4.9 and thickness of 1.6mm respectively. The array antenna was measured in the laboratory using Vector Network Analyzer (VNA) and the results show good agreement with the array antenna simulated performances.

This letter presents novel circular and elliptical coplanar waveguide (CPW)-fed slot and mictrostip-fed antenna designs targeting the 3.1¿10.6 GHz band. The antennas are comprised of elliptical or circular stubs that excite similar-shaped slot apertures. Four prototypes have been examined, fabricated and experimentally tested, the three being fed by a CPW and the fourth by a microstrip line, exhibiting a very satisfactory behavior throughout the 7.5 GHz of the allocated bandwidth in terms of impedance matching $(hbox VSWR¿2)$, radiation efficiency and radiation pattern characteristics. Measured impedance bandwidths of beyond 175% will be presented.

An electrically small, low profile magnetic dipole antenna is presented. The proposed antenna is composed of multiple small twisted loops connected in parallel. The simulated radiation efficiency is 87 % at the resonant frequency of 222.5 MHz. The corresponding electrical size ka is 0.276 with the height of 0.0016λ. The prototype is built using a selective laser sintering technology and silver paste painting. The measured result is discussed.

The characteristics of antipodal Vivaldi antennas with different structures and the key factors that affect the performance of the antipodal antennas have been investigated. The return loss, radiation pattern and current distribution with various elliptical loading sizes, opening rates and ground plane sizes have been analyzed and compared using full wave simulation tool. Based on the parameter study, a design that is capable of achieving a bandwidth from 2.1 GHz to 20 GHz has been identified.

A compact, low cost and high radiation efficiency antenna structure, planar waveguide, substrate integrated waveguide (SIW), dielectric resonator antennas (DRA) is presented in this paper. Since SIW is a high Q- waveguide and DRA is a low loss radiator, then SIW-DRA forms an excellent antenna system with high radiation efficiency at millimeter-waveband, where the conductor loss dominates. The impact of different antenna parameters on the antenna performance is studied. Experimental data for SIW-DRA, based on two different slot orientations, at millimeter-wave band are introduced and compared to the simulated HFSS results to validate our proposed antenna model. A good agreement is obtained. The measured gain for SIW-DRA single element showed a broadside gain of 5.51 dB,-19 dB maximum cross polarized radiation level, and overall calculated (simulated using HFSS) radiation efficiency of greater than 95%.

Introduction: Short-circuited microstrip patch antennas, or PIFAs, can be used as directive internal handset antennas in various communication systems. They provide advantages over traditional external whip and helix antennas in terms of increased total efficiency (when the handset is near the user’s head), decreased radiation towards the user, and increased mechanical reliability. The main disadvantage is their narrow impedance bandwidth compared to the volume occupied by the antenna structure. Typical requirements for directive internal antennas designed for cellular handsets (with a thickness ≤ 20mm) are antenna thickness ≤ 5mm and bandwidth ≥ 10%. It is difficult to design such a small, directive, low-profile handset antenna with high radiation efficiency and an impedance bandwidth greater than 10%. Small antenna design is always a compromise between size, bandwidth, and efficiency. Often, the price for improved performance is increased complexity. If the total dimensions of a patch antenna element are fixed, there are two effective ways to enhance its bandwidth: either dissipative loading [1], which reduces the radiation efficiency, or the addition of more resonators into the antenna structure (matching networks or parasitic elements). The latter method may increase the manufacturing complexity of the antenna. In this Letter, a thin dual-resonant stacked shorted patch antenna is presented. Previously, stacked short-circuited patch antenna elements have been reported in [2, 3]. More recently, similar elements have also been discussed in [4, 5]. The presented antenna is dual-resonant and has a very low profile. The thickness is only one fifth of that of the antenna reported in [5], whereas the surface area reserved for the patches is approximately equal. The small size of the antenna presented in this Letter makes it relatively easy to fit inside a cellular handset. The bandwidth is sufficient for many communication systems. With a common short circuit element which connects both patches to the ground plane, the presented antenna is the simplest example of a stacked shorted microstrip patch antenna. No extra tuning or shorting posts inside the patch area are needed, which makes its manufacturing easier.

In this letter, a novel antenna-in-package (AiP) technology at W-band has been proposed. This technology is presented for solving the special case that the metallic package should be used to accommodate high mechanical strength. By taking advantages of the multilayer low temperature co-fired ceramic (LTCC) technology, the radiation efficiency of the antenna can be maintained. Meanwhile, high mechanical strength and shielding performance are achieved. A prototype of AiP has been designed. The prototype constitutes integrated LTCC antenna, low-loss feeder, and metallic package with a tapered horn aperture. This LTCC feeder is realized by laminated waveguide (LWG). An LWG cavity that is buried in LTCC is employed to broaden the antenna impedance bandwidth. Electromagnetic (EM) simulations and measurements of antenna performances agree well over the whole frequency range of interest. The proposed prototype achieves a -10-dB impedance bandwidth of 10 GHz from 88 to 98 GHz and a peak gain of 12.3 dBi at 89 GHz.

In this paper, a novel approach for the design of a compact broadband planar microstrip patch antenna is presented for millimetre wave wireless applications. This antenna structure was realized on a Quartz crystal substrate of dielectric constant 3.8 with the thickness of 0.8mm with ground plane. In order to examine, the performances of this antenna it was designed at the frequency 30 GHz and simulated by IE3D software package of Zeland. This type of antenna is composed of two slots of different dimensions and fed by single coaxial feed. Appropriate design of the antenna structure resulted in three discontinuous resonant bands. The proposed antenna has a −10dB return loss impedance bandwidth of 10.3GHz. The performance of this antenna was verified and characterised in terms of the antenna return loss, radiation pattern, surface current distribution and power gain. Also, the numerical results show that the proposed antenna has good impedance bandwidth and radiation characteristics over the entire operating bands. Details of the proposed antenna configurations and design procedures are given.

In a variety of microwave and millimeter-wave applications where high performance antennas are required, waveguide slot arrays have received considerable attention due to their high aperture efficiency, low side lobe levels, and low cross polarization. Resonant slot arrays usually suffer from narrow bandwidth and high cost due to high precision required in manufacturing. Furthermore, because of using standard rectangular waveguides, the antenna array is thick and heavy and is not suitable for monolithic integration with high frequency printed circuits.


Question: Design and performance analysis of a low RCS microstrip patch array using electromagnetic methods.

Innovative Metallic Surfaces with High-Impedance and Forbidden Frequency Bands

An advanced metallic electromagnetic structure has been engineered, characterized by its high surface impedance. Despite its ability to conduct direct current (DC), this novel surface does not facilitate alternating current (AC) flow within a specific frequency band, distinguishing it from conventional conductors. Remarkably, it does not support propagating surface waves and displays a non-phase-reversed image current behavior. The structure's design is akin to a corrugated metallic surface, but with the corrugations folded into lumped-circuit elements, arranged in a two-dimensional lattice. This surface can be effectively described using solid-state band theory principles, even when the periodicity is significantly less than the free-space wavelength. Its unique properties make it a promising material for a range of electromagnetic applications, particularly in the development of low-profile antenna designs with enhanced performance characteristics.

In this paper, simulation and measurement results of 0.7-7 GHz double-ridged guide horn (DRGH) antenna with coaxial input feed section is presented. This antenna, due to the large frequency band required by standards, is appropriate to be used in electromagnetic compatibility (EMC) testing and antenna measurement as a transmitter. A step by step method for designing DRGH antenna is given. A suitable taper for the ridges in the horn is designed and its impedance variations along the horn are shown. In addition, a new structure for the electrical field probe in the feed section is introduced by which a shift-down of lower frequency to 0.7 GHz is achieved. A sensitivity analysis is done on the parameters of the proposed structure. Other parameters of the feed section are also investigated and optimized values are obtained. Finally, the proposed antenna has been fabricated and measurement results show good agreement with the simulation.

A new wideband ±45° dual-polarized metal loop dielectric resonator magnetoelectric antenna is proposed in this communication. The antenna consists of two orthogonal dielectric bars that support two orthogonal electric dipoles and two cross interlaced metal semi-loops that are equivalent to two orthogonal magnetic dipoles above the ground. With a legitimate combination of the electric and magnetic dipoles, a unidirectional radiation with low backward radiation and equal E-plane and H-plane radiation patterns can be achieved. The antenna can be made of a monolithic dielectric block with simple installation. To validate the new antenna configuration, a prototype is designed, manufactured, and measured. The prototype antenna using ceramic with the electrical size of <inline-formula> <tex-math notation="LaTeX">$0.33\lambda _{o} \times 0.33\lambda _{o} \times 0.21\lambda _{o}$ </tex-math></inline-formula> demonstrates that an impedance bandwidth of 11.4% (<inline-formula> <tex-math notation="LaTeX">$\vert S_{11}\vert < -15$ </tex-math></inline-formula> dB) in the 3.5 GHz frequency band can be achieved with the measured forward/backward ratio of better than 22 dB. The proposed antenna is suitable as antenna element for massive-MIMO array antennas, where a large number of antenna elements need to be installed in a compact space.

This paper presents a design of band-pass shielding enclosure for two frequency bands of WLAN. The proposed design employs a compact frequency selective surface to highlight the advantages of these miniaturized structures for the selective shielding of such enclosures that are electrically small. The results indicate that the proposed shielding enclosure has high transmittance in both the pass bands for horizontal and vertical polarizations, while offers a good amount of electromagnetic shielding outside these frequency bands.

A compact, low profile, super high antenna gain, and broadband antenna, based on the printed log-periodic dipole array (PLPDA) antenna and the substrate integrated waveguide (SIW) technologies, has been proposed. It is so called substrate integrated waveguide clamped-mode printed log-periodic dipole array (SIW clamped-mode PLPDA) antenna. The arrangement of the dipole elements and feed lines in the SIW clamped-mode PLPDA antenna is designed and optimized. The proposed SIW clamped-mode PLPDA antenna was designed, fabricated, and tested, while a single SIW PLPDA antenna and a 2-element SIW PLPDA array were designed for comparison. The impedance bandwidth and the far-field radiation patterns in both E-plane and H-plane were measured. The measured results show that the performance of the SIW clamped-mode PLPDA antenna is similar to that of the 2-element SIW PLPDA array over the working frequency range 25 GHz-40 GHz, while the proposed antenna has only half the number of dipole elements and feed lines.

A compact cross-shaped slotted microstrip patch antenna is proposed for circularly polarized (CP) radiation. A symmetric, cross shaped slot is embedded along one of the diagonal axes of the square patch for CP radiation and antenna size reduction. The structure is asymmetric (unbalanced) along the diagonal axes. The overall size of the antenna with CP radiation can be reduced by increasing the perimeter of the symmetric cross-shaped slot within the first patch quadrant of the square patch. The performance of the CP radiation is also studied by varying the size and angle variation of the cross-shaped slot. A measured 3-dB axial-ratio (AR) bandwidth of around 6.0 MHz is achieved with the CP cross-shaped slotted microstrip antenna, with an 18.0 MHz 10-dB return-loss bandwidth. The measured boresight gain is more than 3.8 dBic over the operating band, while the overall antenna volume is 0.273λo × 0.273λo × 0.013λo (λο operating wavelength at 910 MHz).

In this paper, a new configuration of integrated 45deg linearly polarized slot array antenna based on substrate integrated waveguide (SIW) technology is proposed, designed and fabricated with normal printed circuit board (PCB) process. The antenna consists of eight 45deg inclined slots and the measured gain is higher than 10.27 dBi at X-band. The measured radiation patterns and the return loss are in agreement with the simulated results.

Analysis of geometrically complicated waveguide-based slotted arrays and filters is performed using a simple equivalent-circuit model. First, the circuit parameters (inductance and capacitance) of a simple waveguide slot-coupler problem are obtained through moment-method (MoM) analysis. The values of the lumped LC elements are virtually constant over the frequency range of interest (the X-band) for specific waveguide and slot dimensions. Based on the equivalent-circuit model of a single slot of two coupled waveguides, more complicated structures are then analyzed, such as slot coupler arrays and slot-based waveguide filters. The scattering parameters of these structures are obtained through circuit analysis, and are verified using the MoM and finite-difference time-domain method. Excellent agreement is observed over a wide band of frequencies and is confirmed by experimental results.

A novel Wideband tapered-slot antenna for GPR applications is proposed. A conventional approach to reduce unwanted interactions between the antenna and the ground surface requires extensive microwave absorbers treatment. For the same purpose we propose to use corrugations along the antenna edges. The corrugations improve directivity and wideband matching of the antenna with free space retaining also good radiation efficiency. Further improvement of the antenna performance has been achieved by patching the corrugated areas of the antenna with the resistance card strips. The elimination of the foam-type absorbers makes the antenna more robust and easier in fabrication.

An ultra-wideband absorber based on employing multiple resonators in the unit cell of a 3-D frequency-selective structure is proposed. The original design consists of two-dimensional periodic array of three-dimensional unit cells and lumped components to provide matching with free space and absorb the incident electromagnetic wave. By introducing one more resonator in the unit cell, an ultra-wideband absorber can be constructed. It has a fractional bandwidth of 155.7 % for −10 dB of reflection coefficient under the normal incidence with an absorber thickness of 0.12 λ0 at the lowest absorption frequency. This absorber works for TE polarization at which two quasi-TEM modes are excited in the air and substrate regions. The proposed absorber shows a stable performance under oblique angles of incidence.

This paper proposes a analysis approach for characterizing the broadband Log Periodic Planar Dipole Array (LPPDA) antenna useful for C-band applications. A simplest geometry of the edge fed Log Periodic Planar Dipole Array is designed and developed. Parameters like length, width of the elements, and spacing between elements are the varied using defined scale factor (τ) and a spacing factor (σ), keeping others constant and their impact was noted down. A parametric analysis is performed. using CST software. A transmission line equivalent circuit of the proposed LPPDA is constructed. Log periodicity behaviour of the proposed antenna is checked by plotting input impedance with respect to logarithm of frequency. An antenna is fabricated and measured in laboratory.


Question: Facial recognition employing Gabor filters and convolutional neural networks.

This paper introduces a novel application of deep learning techniques in medical image analysis, specifically for the classification of lung patterns associated with interstitial lung diseases (ILDs). Automated tissue characterization is a critical aspect of computer-aided diagnosis (CAD) systems for ILDs, but remains a complex challenge. Recent advancements in deep learning have shown remarkable promise in various computer vision tasks, suggesting potential benefits for medical image analysis. Herein, we present and evaluate a deep convolutional neural network (CNN) tailored for ILD pattern classification. The proposed network architecture comprises five convolutional layers, each equipped with 2x2 kernels and LeakyReLU activations, followed by average pooling that matches the size of the final feature maps, and three fully connected layers. The final dense layer outputs seven classes, corresponding to: healthy, ground glass opacity (GGO), micronodules, consolidation, reticulation, honeycombing, and a combination of GGO/reticulation. To train and assess the CNN, we utilized a dataset of 14,696 image patches sourced from 120 CT scans across multiple scanners and hospitals. Our study marks the first instance of a deep CNN specifically designed for this problem. Comparative evaluations showcased the superior performance of the proposed CNN over previous methodologies in a challenging dataset, achieving an accuracy of approximately 85.5%. These findings highlight the potential of CNNs in the analysis of lung patterns. Future research directions include extending the CNN to three-dimensional data derived from CT volume scans and integrating this method into a comprehensive CAD system designed to support radiologists in differential diagnosis for ILDs.

We consider the problem of automatically recognizing human faces from frontal views with varying expression and illumination, as well as occlusion and disguise. We cast the recognition problem as one of classifying among multiple linear regression models and argue that new theory from sparse signal representation offers the key to addressing this problem. Based on a sparse representation computed by C1-minimization, we propose a general classification algorithm for (image-based) object recognition. This new framework provides new insights into two crucial issues in face recognition: feature extraction and robustness to occlusion. For feature extraction, we show that if sparsity in the recognition problem is properly harnessed, the choice of features is no longer critical. What is critical, however, is whether the number of features is sufficiently large and whether the sparse representation is correctly computed. Unconventional features such as downsampled images and random projections perform just as well as conventional features such as eigenfaces and Laplacianfaces, as long as the dimension of the feature space surpasses certain threshold, predicted by the theory of sparse representation. This framework can handle errors due to occlusion and corruption uniformly by exploiting the fact that these errors are often sparse with respect to the standard (pixel) basis. The theory of sparse representation helps predict how much occlusion the recognition algorithm can handle and how to choose the training images to maximize robustness to occlusion. We conduct extensive experiments on publicly available databases to verify the efficacy of the proposed algorithm and corroborate the above claims.

A novel method is proposed in this paper to improve the recognition accuracy of Local Binary Pattern (LBP) on low-resolution face recognition. More precise descriptors and e®ectively face features can be extracted by combining multi-scale blocking center symmetric local binary pattern (CS-LBP) based on Gaussian pyramids and weighted principal component analysis (PCA) on low-resolution condition. Firstly, the features statistical histograms of face images are calculated by multi-scale blocking CS-LBP operator. Secondly, the stronger classi ̄cation and lower dimension features can be got by applying weighted PCA algorithm. Finally, the di®erent classi ̄ers are used to select the optimal classi ̄cation categories of low-resolution face set and calculate the recognition rate. The results in the ORL human face databases show that recognition rate can get 89.38% when the resolution of face image drops to 12 10 pixel and basically satisfy the practical requirements of recognition. The further comparison of other descriptors and experiments from videos proved that the novel algorithm can improve recognition accuracy.

This paper describes a machine learning approach for visual object detection which is capable of processing images extremely rapidly and achieving high detection rates. This work is distinguished by three key contributions. The first is the introduction of a new image representation called the “Integral Image” which allows the features used by our detector to be computed very quickly. The second is a learning algorithm, based on AdaBoost, which selects a small number of critical visual features from a larger set and yields extremely efficient classifiers[6]. The third contribution is a method for combining increasingly more complex classifiers in a “cascade” which allows background regions of the image to be quickly discarded while spending more computation on promising object-like regions. The cascade can be viewed as an object specific focus-of-attention mechanism which unlike previous approaches provides statistical guarantees that discarded regions are unlikely to contain the object of interest. In the domain of face detection the system yields detection rates comparable to the best previous systems. Used in real-time applications, the detector runs at 15 frames per second without resorting to image differencing or skin color detection.

Eye gaze is an important non-verbal cue for human affect analysis. Recent gaze estimation work indicated that information from the full face region can benefit performance. Pushing this idea further, we propose an appearance-based method that, in contrast to a long-standing line of work in computer vision, only takes the full face image as input. Our method encodes the face image using a convolutional neural network with spatial weights applied on the feature maps to flexibly suppress or enhance information in different facial regions. Through extensive evaluation, we show that our full-face method significantly outperforms the state of the art for both 2D and 3D gaze estimation, achieving improvements of up to 14.3% on MPIIGaze and 27.7% on EYEDIAP for person-independent 3D gaze estimation. We further show that this improvement is consistent across different illumination conditions and gaze directions and particularly pronounced for the most challenging extreme head poses.

The track 2 and track 3 of ChaLearn 2016 can be considered as Multi-Label Classification problems. We present a framework of learning deep binary encoding (DeepBE) to deal with multi-label problems by transforming multi-labels to single labels. The transformation of DeepBE is in a hidden pattern, which can be well addressed by deep convolutions neural networks (CNNs). Furthermore, we adopt an ensemble strategy to enhance the learning robustness. This strategy is inspired by its effectiveness in fine-grained image recognition (FGIR) problem, while most of face related tasks such as track 2 and track 3 are also FGIR problems. By DeepBE, we got 5.45% and 10.84% mean square error for track 2 and track 3 respectively. Additionally, we proposed an algorithm adaption method to treat the multiple labels of track 2 directly and got 6.84% mean square error.

Facial action units provide an objective characterization of facial muscle movements. Automatic estimation of facial action unit intensities is a challenging problem given individual differences in neutral face appearances and the need to generalize across different pose, illumination and datasets. In this paper, we introduce the Local-Global Ranking method as a novel alternative to direct prediction of facial action unit intensities. Our method takes advantage of the additional information present in videos and image collections of the same person (e.g. a photo album). Instead of trying to estimate facial expression intensities independently for each image, our proposed method performs a two-stage ranking: a local pair-wise ranking followed by a global ranking. The local ranking is designed to be accurate and robust by making a simple 3-class comparison (higher, equal, or lower) between randomly sampled pairs of images. We use a Bayesian model to integrate all these pair-wise rankings and construct a global ranking. Our Local-Global Ranking method shows state-of-the-art performance on two publicly-available datasets. Our cross-dataset experiments also show better generalizability.

CAPTCHAs (Completely Automated Public Turing test to tell Computers and Humans Apart) are increasingly used in many applications for machine and human identification. Compared with traditional English and digital characters based CAPTCHAs, Chinese characters contain more complicated characters which greatly enhance difficulty of automatic recognition. To solve that problem, we proposed a Convolution Neural Network (CNN) based approach. This approach greatly improves the recognition accuracy of Chinese Character CAPTCHAs with distortion, rotation and background noise. Our experiment results show that this approach achieves more than 95% accuracy for single character and 84% accuracy for three types of Chinese Character CAPTCHAs with four characters. This encouraging result indicates that deep neural network is useful in complicated structure perception of Chinese Character CAPTCHAs.

This paper concerns the estimation of facial attributes-namely, age and gender-from images of faces acquired in challenging, in the wild conditions. This problem has received far less attention than the related problem of face recognition, and in particular, has not enjoyed the same dramatic improvement in capabilities demonstrated by contemporary face recognition systems. Here, we address this problem by making the following contributions. First, in answer to one of the key problems of age estimation research-absence of data-we offer a unique data set of face images, labeled for age and gender, acquired by smart-phones and other mobile devices, and uploaded without manual filtering to online image repositories. We show the images in our collection to be more challenging than those offered by other face-photo benchmarks. Second, we describe the dropout-support vector machine approach used by our system for face attribute estimation, in order to avoid over-fitting. This method, inspired by the dropout learning techniques now popular with deep belief networks, is applied here for training support vector machines, to the best of our knowledge, for the first time. Finally, we present a robust face alignment technique, which explicitly considers the uncertainties of facial feature detectors. We report extensive tests analyzing both the difficulty levels of contemporary benchmarks as well as the capabilities of our own system. These show our method to outperform state-of-the-art by a wide margin.

Deep Neural Networks (DNNs) have progressed significantly in recent years. Novel DNN methods allow tasks such as image and speech recognition to be conducted easily and efficiently, compared with previous methods that needed to search for valid feature values or algorithms. However, DNN computations typically consume a significant amount of time and high-performance computing resources. To facilitate high-speed object recognition, this article introduces a Deep Convolutional Neural Network (DCNN) accelerator based on a field-programmable gate array (FPGA). Our hardware takes full advantage of the characteristics of convolutional calculation; this allowed us to implement all DCNN layers, from image input to classification, in a single chip. In particular, the dateflow from input to classification is uninterrupted and paralleled. As a result, our implementation achieved a speed of 409.62 giga-operations per second (GOPS), which is approximately twice as fast as the latest reported result. Furthermore, we used the same architecture to implement a Recurrent Convolutional Neural Network (RCNN), which can, in theory, provide better recognition accuracy.

Use of Biometrics and widespread acceptability for person authentication has instigated several techniques. Now a day’s security is very important. Biometrics has received a great attention. Face biometrics, is useful person’s authentication that recognizes face. In this paper first we represent an overview of face recognition and discuss the methodology and functioning. Thereafter we represent face recognition techniques which are recently used including their advantages and disadvantages. A large number of face recognition algorithms have been developed. In this paper an attempt is made to review a wide range of methods including PCA, Eigen face, LDA for recognition and hybrid combination if this techniques. Under the various illumination and expression condition of face images some techniques specified here also improves the efficiency of face recognition. Keywords— Face Recognition, Principal Component Analysis, Eigen Face, Linear Discriminant Analysis


Question: Facial identification employing Gabor filter techniques and convolutional neural networks.

This paper introduces a novel face verification technique utilizing a convolutional neural network (CNN) in a "Siamese" architecture, designed to efficiently tackle the face verification challenge. The proposed system features a streamlined architecture, consisting of dual CNNs, each condensed to merely four layers by integrating convolutional and subsampling operations. The training process employs the stochastic gradient descent algorithm, adjusted with an annealed global learning rate for optimization. The network's generalization capability is evaluated through unique image pairing, with testing conducted on the AT&T face database. Results demonstrate the system's exceptional speed, capable of classifying pairs of 46×46 pixel face images in a mere 0.6 milliseconds, significantly outpacing networks with cascaded convolutional and subsampling layers. The achieved equal error rate (EER) for verification accuracy is 3.33%, showcasing the model's efficacy. Notably, learning convergence is reached within 20 epochs, and the method proves capable of verifying test subjects not encountered during training. This study highlights the effectiveness of the "Siamese" CNN for face verification tasks, with ongoing efforts aimed at refining the architecture to further enhance its performance.

The Face Expression Based Emoticon Identification (FEBEI) system is an open source extension to the Tracker.js framework which converts a human facial expression to the best matching emoticon. The contribution of this project was to build this robust classifier which can identify facial expression in real time without any reliance on an external server or computation node. An entirely client-side JavaScript implementation has clear privacy benefits as well as the avoidance of any lag inherent in uploading and downloading images. We accomplished this by utilizing several computationally efficient methods. Tracking.js provided a Viola Jones based face detector which we used to pass facial images to our own implementation of an eigenemotion detection system which was trained to distinguish between happy and angry faces. We have implemented a similar eigenface classifier in python and have trained a Convoluted Neural Network (CNN) to classify emotions to provide a comparative view of its advantages. We aim to make FEBEI easily extendable so that the developer community will be able to add classifiers for more emoticon.

Face recognition is receiving a significant attention due to the need of facing important challenges when developing real applications under unconstrained environments. The three most important challenges are facial occlusion, the problem of dealing with a single sample per subject (SSPS) and facial expression. This paper describes and analyzes various strategies that have been developed recently for overcoming these three major challenges that seriously affect the performance of real face recognition systems. This survey is organized in three parts. In the first part, approaches to tackle the challenge of facial occlusion are classified, illustrated and compared. The second part briefly describes the SSPS problem and the associated solutions. In the third part, facial expression challenge is illustrated. In addition, pros and cons of each technique are stated. Finally, several improvements for future research are suggested, providing a useful perspective for addressing new research in face recognition.

In this paper, we present a new database acquired with three different sensors (visible, near infrared and thermal) under different illumination conditions. This database consists of 41 people acquired in four different acquisition sessions, five images per session and three different illumination conditions. The total amount of pictures is 7,380 pictures. Experimental results consist of single sensor experiments as well as the combination of two and three sensors under different illumination conditions (natural, infrared and artificial illumination). We have found that the three studied spectral bands contribute in a nearly equal proportion to a combined system. Experimental results show a significant improvement combining the three spectrums, even when using a simple classifier and feature extractor. In six of the nine studied scenarios, we obtained identification rates higher or equal to 98 %, when using a trained combination rule, and two cases of nine when using a fixed rule.

Automatic face recognition technologies have seen significant improvements in performance due to a combination of advances in deep learning and availability of larger datasets for training deep networks. Since recognizing faces is a task that humans are believed to be very good at, it is only natural to compare the relative performance of automated face recognition and humans when processing fully unconstrained facial imagery. In this work, we expand on previous studies of the recognition accuracy of humans and automated systems by performing several novel analyses utilizing unconstrained face imagery. We examine the impact on performance when human recognizers are presented with varying amounts of imagery per subject, immutable attributes such as gender, and circumstantial attributes such as occlusion, illumination, and pose. Results indicate that humans greatly outperform state of the art automated face recognition algorithms on the challenging IJB-A dataset.

In this paper, weinvestigaterecognition of humanfaces in a meetingroom. The major challenges of identifying humanfacesin this environmentincludelow quality of input images,poor illumination,unrestrictedheadposesand continuouslychangingfacial expressionsandocclusion.In order to addresstheseproblemswe proposea novel algorithm, DynamicSpaceWarping (DSW).Thebasic idea of the algorithm is to combinelocal features under certain spatial constraints. We compare DSWwith the eigenface approachondatacollectedfromvariousmeetings.Wehave testedboth front and profile face imagesand imageswith two stagesof occlusion.Theexperimentalresultsindicate thattheDSWapproachoutperformstheeigenfaceapproach in bothcases.

The randomness and richness present in the iris texture make the 2D Gabor filter bank analysis a suitable technique to be used for iris recognition systems. To accurately characterize complex texture structures using 2D Gabor filters it is necessary to use multiple sets of parameters of this type of filters. This paper proposes a technique of optimizing multiple sets of 2D Gabor filter parameters to gradually enhance the accuracy of an iris recognition system. The proposed methodology is suitable to be applied on both near infrared and visible spectrum iris images. To illustrate the efficiency of the filter bank design technique, UBIRISv1 database was used for benchmarking.

Temporal features is important for face anti-spoofing. Unfortunately existing methods have limitations to explore such temporal features. In this work, we propose a deep neural network architecture combining Long Short-Term Memory (LSTM) units with Convolutional Neural Networks (CNN). Our architecture works well for face anti-spoofing by utilizing the LSTM units' ability of finding long relation from its input sequences as well as extracting local and dense features through convolution operations. Our best model shows significant performance improvement over general CNN architecture (5.93% vs. 7.34%), and hand-crafted features (5.93% vs. 10.00%) on CASIA dataset.

We propose a deep convolutional neural network (CNN) for face detection leveraging on facial attributes based supervision. We observe a phenomenon that part detectors emerge within CNN trained to classify attributes from uncropped face images, without any explicit part supervision. The observation motivates a new method for finding faces through scoring facial parts responses by their spatial structure and arrangement. The scoring mechanism is data-driven, and carefully formulated considering challenging cases where faces are only partially visible. This consideration allows our network to detect faces under severe occlusion and unconstrained pose variations. Our method achieves promising performance on popular benchmarks including FDDB, PASCAL Faces, AFW, and WIDER FACE.

Face recognition presents a challenging problem in the field of image analysis and computer vision, and as such has received a great deal of attention over the last few years because of its many applications in various domains. Face recognition techniques can be broadly divided into three categories based on the face data acquisition methodology: methods that operate on intensity images; those that deal with video sequences; and those that require other sensory data such as 3D information or infra-red imagery. In this paper, an overview of some of the well-known methods in each of these categories is provided and some of the benefits and drawbacks of the schemes mentioned therein are examined. Furthermore, a discussion outlining the incentive for using face recognition, the applications of this technology, and some of the difficulties plaguing current systems with regard to this task has also been provided. This paper also mentions some of the most recent algorithms developed for this purpose and attempts to give an idea of the state of the art of face recognition technology.

Facial expressions play a significant role in human dialogue. As a result, there has been considerable work done on the recognition of emotional expressions and the application of this research will be beneficial in improving human-machine dialogue. One can imagine the improvements to computer interfaces, automated clinical (psychological) research or even interactions between humans and autonomous robots.


Question: Facial recognition implementation with Gabor filter and convolutional neural network.

In this study, we introduce an enhanced face recognition technique that integrates Gabor filters and convolutional neural networks (CNNs). Our method begins with the utilization of Gabor filters to extract key facial features, yielding four distinct subimages. Subsequently, these subimages are fed into a CNN for further processing. The CNN analyzes the subimages, refining the extracted features and enhancing the recognition accuracy. Our proposed method surpasses the performance of a standalone CNN, demonstrating its effectiveness in facial recognition tasks. By combining the strengths of Gabor filters and CNNs, our approach achieves superior classification results, particularly in scenarios where facial features are critical for accurate recognition.

In this paper we present a comprehensive and critical survey of face detection algorithms. Face detection is a necessary first-step in face recognition systems, with the purpose of localizing and extracting the face region from the background. It also has several applications in areas such as content-based image retrieval, video coding, video conferencing, crowd surveillance, and intelligent human–computer interfaces. However, it was not until recently that the face detection problem received considerable attention among researchers. The human face is a dynamic object and has a high degree of variability in its apperance, which makes face detection a difficult problem in computer vision. A wide variety of techniques have been proposed, ranging from simple edge-based algorithms to composite high-level approaches utilizing advanced pattern recognition methods. The algorithms presented in this paper are classified as either feature-based or image-based and are discussed in terms of their technical approach and performance. Due to the lack of standardized tests, we do not provide a comprehensive comparative evaluation, but in cases where results are reported on common datasets, comparisons are presented. We also give a presentation of some proposed applications and possible application areas. c © 2001 Academic Press

The goal of this paper is to implement an efficient FPGA-based hardware architectures for the design of fast artificial vision systems. The proposed architecture is capable of performing classification operations of a Convolutional Neural Network (CNN) in realtime. To show the effectiveness of the architecture, some design examples such as hand posture recognition, character recognition, and face recognition are provided. Experimental results show that the proposed architecture is well suited for embedded artificial computer vision systems requiring high portability, high computational speed, and accurate classification.

Face detection is one of the challenging problems in image processing. This report proposes a novel technique for detecting faces in color images using skin color model algorithm combined with skin likely-hood, skin Segmentation, Morphological operation and Template matching. Color images with skin color in the chromatic and pure color space YCrCb, which separates luminance and chrominance components. A Gaussian probability density is estimated from skin samples, collected from different ethnic groups, via the maximum-likelihood criterion. Adaptive thresholding for segmentation to localize the faces within the detected skin regions. Further, mathematical morphological operators are used to remove noisy regions and fill holes in the skin-color region, so that candidate human face regions can be extracted. These systems can achieve high detection accuracy, high detection speed and reduce the false detecting rate. The two methodology used for the Skin Segmentation is YCbCr and RGB Model. In YCbCr model both the skin colour and texture of the image can be used to identify the particular object in the image,where as in RGB model only the skin color has to be used for identification of the person.Hence the YCbCr model is better than the RGB model . From our analysis we conclude that the new approach in modeling skin color can achieve good detection success rate. The algorithm gives computationally a very efficient as well as an accurate approach for skin detection. The performance of different color space may be dependent on the method used to model the color for skin pixel. Keywords—Gaussian probability, YCrCb,RGB Model

Facial Expression Recognition is still one of the challenging fields in pattern recognition and machine learning science. Despite efforts made in developing various methods for this topic, existing approaches lack generalizability and almost all studies focus on more traditional hand-crafted features extraction to characterize facial expressions. Moreover, effective classifiers to model the spatial and temporary patterns embedded in facial expressions ignore the effects of facial attributes, such as age, on expression recognition even though research indicates that facial expression manifestation varies with ages. Although there are large amount of benchmark datasets available for the recognition of facial expressions, only few datasets contains faces of older adults. Consequently the current scientific literature has not exhausted this topic. Recently, deep learning methods have been attracting more and more researchers due to their great success in various computer vision tasks, mainly because they avoid a process of feature definition and extraction which is often very difficult due to the wide variability of the facial expressions. Based on the deep learning theory, a neural network for facial expression recognition in older adults is constructed by combining a Stacked Denoising Auto-Encoder method to pre-train the network and a supervised training that provides a fine-tuning adjustment of the network. For the supervised classification layer, the -class softmax classifier was implemented, where is the number of expressions to be recognized. The performance are evaluated on two benchmark datasets (FACES and Lifespan), that are the only ones that contain facial expressions of the elderly. The achieved results show the superiority of the proposed deep learning approach compared to the conventional non-deep learning based facial expression recognition methods used in this context.

Recently Viola et al. [5] have introduced a rapid object detection scheme based on a boosted cascade of simple features. In this paper we introduce a novel set of rotated haar-like features, which significantly enrich this basic set of simple haar-like features and which can also be calculated very efficiently. At a given hit rate our sample face detector shows off on average a 10% lower false alarm rate by means of using these additional rotated features. We also present a novel post optimization procedure for a given boosted cascade improving on average the false alarm rate further by 12.5%. Using both enhancements the number of false detections is only 24 at a hit rate of 82.3% on the CMU face set [7].

This paper proposes a robust approach for face detection and gender classification in color images. Previous researches about gender recognition suppose an expensive computational and time-consuming pre-processing step in order to alignment in which face images are aligned so that facial landmarks like eyes, nose, lips, chin are placed in uniform locations in image. In this paper, a novel technique based on mathematical analysis is represented in three stages that eliminates alignment step. First, a new color based face detection method is represented with a better result and more robustness in complex backgrounds. Next, the features which are invariant to affine transformations are extracted from each face using scale invariant feature transform (SIFT) method. To evaluate the performance of the proposed algorithm, experiments have been conducted by employing a SVM classifier on a database of face images which contains 500 images from distinct people with equal ratio of male and female.

An age group classification system for gray-scale facial images is proposed in this paper. Four age groups, including babies, young adults, middle-aged adults, and old adults, are used in the classification system. The process of the system is divided into three phases: location, feature extraction, and age classification. Based on the symmetry of human faces and the variation of gray levels, the positions of eyes, noses, and mouths could be located by applying the Sobel edge operator and region labeling. Two geometric features and three wrinkle features from a facial image are then obtained. Finally, two back-propagation neural networks are constructed for classification. The first one employs the geometric features to distinguish whether the facial image is a baby. If it is not, then the second network uses the wrinkle features to classify the image into one of three adult groups. The proposed system is experimented with 230 facial images on a Pentium II 350 processor with 128 MB RAM. One half of the images are used for training and the other half for test. It takes 0.235 second to classify an image on an average. The identification rate achieves 90.52% for the training images and 81.58% for the test images, which is roughly close to human’s subjective justification.

A facial recognition system is a computer application for automatically identifying or verifying a person from a digital image or a video frame from a video source. One of the way is to do this is by comparing selected facial features from the image and a facial database. It is typically used in security systems and can be compared to other biometrics such as fingerprint or eye iris recognition systems. In this paper we focus on 3-D facial recognition system and biometric facial recognition system. We do critics on facial recognition system giving effectiveness and weaknesses. This paper also introduces scope of recognition system in India. Face recognition has received substantial attention from researchers in biometrics, pattern recognition field and computer vision communities. Face recognition can be applied in Security measure at Air ports, Passport verification, Criminals list verification in police department, Visa processing, Verification of Electoral identification and Card Security measure at ATM’s. Principal Component Analysis (PCA) is a technique among the most common feature extraction techniques used in Face Recognition. In this paper, a face recognition system for personal identification and verification using Principal Component Analysis with different distance classifiers is proposed. The test results in the ORL face database produces interesting results from the point of view of recognition success, rate, and robustness of the face recognition algorithm. Different classifiers were used to match the image of a person to a class (a subject) obtained from the training data. These classifiers are: the City-Block Distance Classifier, the Euclidian distance classifier, the Squared Euclidian Distance Classifier, and the Squared Chebyshev distance Classifier.

At present there are many methods that could deal well with frontal view face recognition. However, most of them cannot work well when there is only one training image per person. In this paper, an extension of the eigenface technique, i.e. (PC)A, is proposed. (PC)A combines the original face image with its horizontal and vertical projections and then performs principal component analysis on the enriched version of the image. It requires less computational cost than the standard eigenface technique and experimental results show that on a gray-level frontal view face database where each person has only one training image, (PC)A achieves 3%-5% higher accuracy than the standard eigenface technique through using 10%-15% fewer eigenfaces.

This paper presents an architecture of a low-power real-time object detection processor using Adaboost with Haar-Like features. We employ a register array based architecture, and introduce two architectural-level power optimization techniques; signal gating domain for integral image extraction, and low-power integral image update. The power efficiency of our proposed architecture including nine classifiers is estimated to be 0.64mW/fps when handling VGA(640 × 480) 70fps video.


Question: Facial recognition employing Gabor filters and convolutional neural networks.

This paper offers an extensive overview of face detection techniques in images, a critical component for advanced vision-based human-computer interaction systems. Face detection is a foundational step for various applications including recognition, tracking, pose estimation, and expression analysis. Many sophisticated methods, however, presuppose that faces within images or sequences have already been localized and identified. To construct fully automated systems capable of analyzing facial information, robust and efficient face detection algorithms are indispensable. The primary challenge in face detection involves identifying all regions within an image that contain a face, irrespective of its position, orientation, lighting, or the inherent variability in size, shape, color, and texture that human faces exhibit. Various methods have been devised to address this challenge, and this paper aims to systematically categorize and assess these algorithms. Additionally, we address pertinent topics such as data collection strategies, evaluation metrics, and benchmarking practices. After a thorough analysis of these techniques and their limitations, we conclude by highlighting several promising avenues for future research and development in face detection technology. Specifically, we explore the integration of Gabor filters and convolutional neural networks (CNNs) for enhanced face recognition capabilities, which is a novel direction that combines texture analysis and deep learning approaches for improved accuracy and efficiency in facial recognition systems.

Neural networks are a powerful technology for classification of visual inputs arising from documents. However, there is a confusing plethora of different neural network methods that are used in the literature and in industry. This paper describes a set of concrete best practices that document analysis researchers can use to get good results with neural networks. The most important practice is getting a training set as large as possible: we expand the training set by adding a new form of distorted data. The next most important practice is that convolutional neural networks are better suited for visual document tasks than fully connected networks. We propose that a simple “do-it-yourself” implementation of convolution with a flexible architecture is suitable for many visual document problems. This simple convolutional neural network does not require complex methods, such as momentum, weight decay, structuredependent learning rates, averaging layers, tangent prop, or even finely-tuning the architecture. The end result is a very simple yet general architecture which can yield state-of-the-art performance for document analysis. We illustrate our claims on the MNIST set of English digit

We propose a classification technique for face expression recognition using AdaBoost that learns by selecting the relevant global and local appearance features with the most discriminating information. Selectivity reduces the dimensionality of the feature space that in turn results in significant speed up during online classification. We compare our method with another leading margin-based classifier, the Support Vector Machines (SVM) and identify the advantages of using AdaBoost over SVM in this context. We use histograms of Gabor and Gaussian derivative responses as the appearance features. We apply our approach to the face expression recognition problem where local appearances play an important role. Finally, we show that though SVM performs equally well, AdaBoost feature selection provides a final hypothesis model that can easily be visualized and interpreted, which is lacking in the high dimensional support vectors of the SVM.

The accuracy of automated human face recognition algorithms can significantly degrade while recognizing same subjects under make-up and disguised appearances. Increasing constraints on enhanced security and surveillance requires enhanced accuracy from face recognition algorithms for faces under disguise and/or makeup. This paper presents a new database for face images under disguised and make-up appearances the development of face recognition algorithms under such covariates. This database has 2460 images from 410 different subjects and is acquired under real environment, focuses on make-up and disguises covariates and also provides ground truth (eye glass, goggle, mustache, beard) for every image. This can enable developed algorithms to automatically quantify their capability for identifying such important disguise attribute during the face recognition We also present comparative experimental results from two popular commercial matchers and from recent publications. Our experimental results suggest significant performance degradation in the capability of these matchers in automatically recognizing these faces. We also analyze face detection accuracy from these matchers. The experimental results underline the challenges in recognizing faces under these covariates. Availability of this new database in public domain will help to advance much needed research and development in recognizing make-up and disguised faces.

Convolutional neural networks (CNNs) have massively impacted visual recognition in 2D images, and are now ubiquitous in state-of-the-art approaches. While CNNs naturally extend to other domains, such as audio and video, where data is also organized in rectangular grids, they do not easily generalize to other types of data such as 3D shape meshes, social network graphs or molecular graphs. To handle such data, we propose a novel graph-convolutional network architecture that builds on a generic formulation that relaxes the 1-to-1 correspondence between filter weights and data elements around the center of the convolution. The main novelty of our architecture is that the shape of the filter is a function of the features in the previous network layer, which is learned as an integral part of the neural network. Experimental evaluations on digit recognition, semi-supervised document classification, and 3D shape correspondence yield state-of-the-art results, significantly improving over previous work for shape correspondence.

We describe a deep learning based method for estimating 3D facial expression coefficients. Unlike previous work, our process does not relay on facial landmark detection methods as a proxy step. Recent methods have shown that a CNN can be trained to regress accurate and discriminative 3D morphable model (3DMM) representations, directly from image intensities. By foregoing landmark detection, these methods were able to estimate shapes for occluded faces appearing in unprecedented viewing conditions. We build on those methods by showing that facial expressions can also be estimated by a robust, deep, landmark-free approach. Our ExpNet CNN is applied directly to the intensities of a face image and regresses a 29D vector of 3D expression coefficients. We propose a unique method for collecting data to train our network, leveraging on the robustness of deep networks to training label noise. We further offer a novel means of evaluating the accuracy of estimated expression coefficients: by measuring how well they capture facial emotions on the CK+ and EmotiW-17 emotion recognition benchmarks. We show that our ExpNet produces expression coefficients which better discriminate between facial emotions than those obtained using state of the art, facial landmark detectors. Moreover, this advantage grows as image scales drop, demonstrating that our ExpNet is more robust to scale changes than landmark detectors. Finally, our ExpNet is orders of magnitude faster than its alternatives.

The Face Expression Based Emoticon Identification (FEBEI) system is an open source extension to the Tracker.js framework which converts a human facial expression to the best matching emoticon. The contribution of this project was to build this robust classifier which can identify facial expression in real time without any reliance on an external server or computation node. An entirely client-side JavaScript implementation has clear privacy benefits as well as the avoidance of any lag inherent in uploading and downloading images. We accomplished this by utilizing several computationally efficient methods. Tracking.js provided a Viola Jones based face detector which we used to pass facial images to our own implementation of an eigenemotion detection system which was trained to distinguish between happy and angry faces. We have implemented a similar eigenface classifier in python and have trained a Convoluted Neural Network (CNN) to classify emotions to provide a comparative view of its advantages. We aim to make FEBEI easily extendable so that the developer community will be able to add classifiers for more emoticon.

CNNs have massively improved performance in object detection in photographs. However research into object detection in artwork remains limited. We show state-of-the-art performance on a challenging dataset, People-Art, which contains people from photos, cartoons and 41 different artwork movements. We achieve this high performance by fine-tuning a CNN for this task, thus also demonstrating that training CNNs on photos results in overfitting for photos: only the first three or four layers transfer from photos to artwork. Although the CNN’s performance is the highest yet, it remains less than 60% AP, suggesting further work is needed for the cross-depiction problem.

The aim of an automatic video-based facial expression recognition system is to detect and classify human facial expressions from image sequence. An integrated automatic system often involves two components: 1) peak expression frame detection and 2) expression feature extraction. In comparison with the image-based expression recognition system, the video-based recognition system often performs online detection, which prefers low-dimensional feature representation for cost-effectiveness. Moreover, effective feature extraction is needed for classification. Many recent recognition systems often incorporate rich additional subjective information and thus become less efficient for real-time application. In our facial expression recognition system, first, we propose the double local binary pattern (DLBP) to detect the peak expression frame from the video. The proposed DLBP method has a much lower-dimensional size and can successfully reduce detection time. Besides, to handle the illumination variations in LBP, logarithm-laplace (LL) domain is further proposed to get a more robust facial feature for detection. Finally, the Taylor expansion theorem is employed in our system for the first time to extract facial expression feature. We propose the Taylor feature pattern (TFP) based on the LBP and Taylor expansion to obtain an effective facial feature from the Taylor feature map. Experimental results on the JAFFE and Cohn-Kanade data sets show that the proposed TFP method outperforms some state-of-the-art LBP-based feature extraction methods for facial expression feature extraction and can be suited for real-time applications.

Face image quality can be defined as a measure of the utility of a face image to automatic face recognition. In this work, we propose (and compare) two methods for automatic face image quality based on target face quality values from (i) human assessments of face image quality (matcherindependent), and (ii) quality values computed from similarity scores (matcher-dependent). A support vector regression model trained on face features extracted using a deep convolutional neural network (ConvNet) is used to predict the quality of a face image. The proposed methods are evaluated on two unconstrained face image databases, LFW and IJB-A, which both contain facial variations with multiple quality factors. Evaluation of the proposed automatic face image quality measures shows we are able to reduce the FNMR at 1% FMR by at least 13% for two face matchers (a COTS matcher and a ConvNet matcher) by using the proposed face quality to select subsets of face images and video frames for matching templates (i.e., multiple faces per subject) in the IJB-A protocol. To our knowledge, this is the first work to utilize human assessments of face image quality in designing a predictor of unconstrained face quality that is shown to be effective in cross-database evaluation.

Human visual object recognition is typically rapid and seemingly effortless, as well as largely independent of viewpoint and object orientation. Until very recently, animate visual systems were the only ones capable of this remarkable computational feat. This has changed with the rise of a class of computer vision algorithms called deep neural networks (DNNs) that achieve human-level classification performance on object recognition tasks. Furthermore, a growing number of studies report similarities in the way DNNs and the human visual system process objects, suggesting that current DNNs may be good models of human visual object recognition. Yet there clearly exist important architectural and processing differences between stateof-the-art DNNs and the primate visual system. The potential behavioural consequences of these differences are not well understood. We aim to address this issue by comparing human and DNN generalisation abilities towards image degradations. We find the human visual system to be more robust to image manipulations like contrast reduction, additive noise or novel eidolon-distortions. In addition, we find progressively diverging classification error-patterns between man and DNNs when the signal gets weaker, indicating that there may still be marked differences in the way humans and current DNNs perform visual object recognition. We envision that our findings as well as our carefully measured and freely available behavioural datasets1 provide a new useful benchmark for the computer vision community to improve the robustness of DNNs and a motivation for neuroscientists to search for mechanisms in the brain that could facilitate this robustness. Data and materials available at https://github.com/rgeirhos/object-recognition 1 ar X iv :1 70 6. 06 96 9v 1 [ cs .C V ] 2 1 Ju n 20 17


Question: Facial recognition employing Gabor filters and convolutional neural networks.

This study explores the influence of convolutional network depth on its precision within the context of large-scale image recognition. Our primary contribution involves an exhaustive assessment of progressively deeper networks, revealing that notable enhancements over established configurations can be attained by increasing the depth to 16-19 weight layers. These discoveries served as the foundation for our submission to the ImageNet Challenge 2014, where our team secured the top positions in both the localization and classification categories. Additionally, we demonstrate that our models' representations effectively generalize to alternative datasets, achieving cutting-edge outcomes. Of significant importance, we have released our two most advanced ConvNet models to the public, fostering further exploration into the application of deep visual representations within the computer vision domain.

In a related context, the utilization of Gabor filters in conjunction with Convolutional Neural Networks (CNNs) for face recognition is an area of recent focus. This methodology combines the texture analysis capabilities of Gabor filters with the hierarchical feature learning of CNNs to enhance facial feature extraction. The integration of these techniques aims to improve the robustness and accuracy of face recognition systems, particularly in challenging conditions such as varying lighting and pose. Through the implementation of very deep CNN architectures enhanced by Gabor filters, researchers are pushing the boundaries of facial recognition technology, achieving state-of-the-art performance in multiple benchmarks. This synergistic approach not only optimizes the utilization of deep learning models for facial feature recognition but also showcases the potential for further advancements in biometric identification and computer vision applications.

We develop a method for emotion recognition from facial imagery. This problem is challenging in part because of the subjectivity of ground truth labels and in part because of the relatively small size of existing labeled datasets. We use the FER+ dataset [8], a dataset with multiple emotion labels per image, in order to build an emotion recognition model that encompasses a full range of emotions. Since the amount of data in the FER+ dataset is limited, we explore the use of a much larger face dataset, MS-Celeb-1M [41], in conjunction with the FER+ dataset. Specific layers within an Inception-ResNet-v1 [13, 38] model trained for facial recognition are used for the emotion recognition problem. Thus, we leverage the MS-Celeb-1M dataset in addition to the FER+ dataset and experiment with different architectures to assess the overall performance of neural networks to recognize emotion using facial imagery.

The objective of this paper is to develop the image processing and recognize the faces using PCA-based face recognition technique. MATLAB based programs are implemented to identify the faces using Indian databases and the Face recognition data, University of Essex, UK. For matching unknown images with known images, different techniques like sum of absolute difference (SAD), sum of squared difference (SSD), normalized cross correlation (NCC) etc. can be used which has been shown in this paper. Finally, it has been shown that the performance of PCA-based face recognition system is quite satisfactory instead of having some shortcomings of the system. (Abstract)

The advantages of Convolutional Neural Networks (CNNs) with respect to traditional methods for visual pattern recognition have changed the field of machine vision. The main issue that hinders broad adoption of this technique is the massive computing workload in CNN that prevents real-time implementation on low-power embedded platforms. Recently, several dedicated solutions have been proposed to improve the energy efficiency and throughput, nevertheless the huge amount of data transfer involved in the processing is still a challenging issue. This work proposes a new CNN accelerator exploiting a novel memory access scheme which significantly improves data locality in CNN related processing. With this scheme, external memory access is reduced by 50% while achieving similar or even better throughput. The accelerator is implemented using 28nm CMOS technology. Implementation results show that the accelerator achieves a performance of 102GOp/s @800MHz while consuming 0.303mm2 in silicon area. Power simulation shows that the dynamic power of the accelerator is 68mW. Its flexibility is demonstrated by running various different CNN benchmarks.

Proc. of the International Conference on Computer Vision, Corfu (Sept. 1999) An object recognition system has been developed that uses a new class of local image features. The features are invariant to image scaling, translation, and rotation, and partially invariant to illumination changes and affine or 3D projection. These features share similar properties with neurons in inferior temporal cortex that are used for object recognition in primate vision. Features are efficiently detected through a staged filtering approach that identifies stable points in scale space. Image keys are created that allow for local geometric deformations by representing blurred image gradients in multiple orientation planes and at multiple scales. The keys are used as input to a nearest-neighbor indexing method that identifies candidate object matches. Final verification of each match is achieved by finding a low-residual least-squares solution for the unknown model parameters. Experimental results show that robust object recognition can be achieved in cluttered partially-occluded images with a computation time of under 2 seconds.

In this paper, we study oracle character recognition and general sketch recognition. First, a data set of oracle characters, which are the oldest hieroglyphs in China yet remain a part of modern Chinese characters, is collected for analysis. Second, typical visual representations in shape- and sketch-related works are evaluated. We analyze the problems suffered when addressing these representations and determine several representation design criteria. Based on the analysis, we propose a novel hierarchical representation that combines a Gabor-related low-level representation and a sparse-encoder-related mid-level representation. Extensive experiments show the effectiveness of the proposed representation in both oracle character recognition and general sketch recognition. The proposed representation is also complementary to convolutional neural network (CNN)-based models. We introduce a solution to combine the proposed representation with CNN-based models, and achieve better performances over both approaches. This solution has beaten humans at recognizing general sketches.

The goal of this paper is to implement an efficient FPGA-based hardware architectures for the design of fast artificial vision systems. The proposed architecture is capable of performing classification operations of a Convolutional Neural Network (CNN) in realtime. To show the effectiveness of the architecture, some design examples such as hand posture recognition, character recognition, and face recognition are provided. Experimental results show that the proposed architecture is well suited for embedded artificial computer vision systems requiring high portability, high computational speed, and accurate classification.

Biometric systems are quite common in our everyday life. Despite the higher difficulty to circumvent them, nowadays criminals are developing techniques to accurately simulate physical, physiological, and behavioral traits of valid users, process known as spoofing attack. In this context, robust countermeasure methods must be developed and integrated with the traditional biometric applications in order to prevent such frauds. Despite face being a promising trait due to its convenience and acceptability, face recognition systems can be easily fooled with common printed photographs. Most of state-of-the-art antispoofing techniques for face recognition applications extract handcrafted texture features from images, mainly based on the efficient local binary patterns (LBP) descriptor, to characterize them. However, recent results indicate that high-level (deep) features are more robust for such complex tasks. In this brief, a novel approach for face spoofing detection that extracts deep texture features from images by integrating the LBP descriptor to a modified convolutional neural network is proposed. Experiments on the NUAA spoofing database indicate that such deep neural network (called LBPnet) and an extended version of it (n-LBPnet) outperform other state-of-the-art techniques, presenting great results in terms of attack detection.

In this paper, we propose a method to estimate head pose with convolutional neural network, which is trained on synthetic head images. We formulate head pose estimation as a regression problem. A convolutional neural network is trained to learn head features and solve the regression problem. To provide annotated head poses in the training process, we generate a realistic head pose dataset by rendering techniques, in which we consider the variation of gender, age, race and expression. Our dataset includes 74000 head poses rendered from 37 head models. For each head pose, RGB image and annotated pose parameters are given. We evaluate our method on both synthetic and real data. The experiments show that our method improves the accuracy of head pose estimation.

In this thesis, the deep learning method convolutional neural networks (CNNs) has been used in an attempt to solve two classification problems, namely traffic sign recognition and Alzheimer’s disease detection. The two datasets used are from the German Traffic Sign Recognition Benchmark (GTSRB) and the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The final test results on the traffic sign dataset generated a classification accuracy of 98.81 %, almost as high as human performance on the same dataset, 98.84 %. Different parameter settings of the selected CNN structure have also been tested in order to see their impact on the classification accuracy. Trying to distinguish between MRI images of healthy brains and brains afflicted with Alzheimer’s disease gained only about 65 % classification accuracy. These results show that the convolutional neural network approach is very promising for classifying traffic signs, but more work needs to be done when working with the more complex problem of detecting Alzheimer’s disease.

Latest results indicate that features learned via convolutional neural networks outperform previous descriptors on classification tasks by a large margin. It has been shown that these networks still work well when they are applied to datasets or recognition tasks different from those they were trained on. However, descriptors like SIFT are not only used in recognition but also for many correspondence problems that rely on descriptor matching. In this paper we compare features from various layers of convolutional neural nets to standard SIFT descriptors. We consider a network that was trained on ImageNet and another one that was trained without supervision. Surprisingly, convolutional neural networks clearly outperform SIFT on descriptor matching.