Question: Design of a 50-kV 100-kW three-phase resonant converter for use in a 95-GHz gyrotron.

This paper details the design and implementation of a cutting-edge repetitive impulse voltage generator utilizing a boost converter array. To overcome the limitations of conventional generators, including short operational lifespan, low frequency, and fixed pulse width, the new generator features a boost converter array with series-connected capacitors and insulated gate bipolar transistors (IGBTs). This innovative circuit design facilitates the generation of high-voltage pulses without the need for a high-voltage direct current source or a pulse transformer. As a result, the proposed circuit not only eliminates the need for an expensive high-frequency transformer but also enables reliable operation at frequencies up to several kilohertz with an extended service life. To substantiate the efficacy of the proposed circuit, two pulse generators, rated at 1.8 kV, 40 A and 20 kV, 300 A, have been constructed and tested, demonstrating the circuit's capabilities and potential applications.

**Question**: A Direct Search Method to solve Economic Dispatch Problem with Valve-Point Effect

**Documents**: A hybrid of genetic algorithm and particle swarm optimization for recurrent network design

**Similar Documents**: This paper presents an innovative hybrid approach, combining genetic algorithms (GA) and particle swarm optimization (PSO), aimed at optimizing the design of recurrent neural networks. The proposed method, termed HGAPSO, integrates the strengths of both GA and PSO to generate new generations of network designs. In HGAPSO, not only are traditional crossover and mutation operations applied as in GA, but PSO principles are also employed. A key feature of HGAPSO is the elite strategy, where the top-performing individuals (elites) are not simply replicated but are further enhanced through PSO, mimicking natural selection. This process of enhancement ensures that the elites contribute to the next generation in a more refined state. Half of the new generation is composed of these enhanced elites, while the other half is produced by applying crossover and mutation operations on them. The effectiveness of HGAPSO is demonstrated through its application to recurrent neural network design, specifically in addressing a temporal sequence production problem. Additionally, HGAPSO is used to design a Takagi-Sugeno-Kang-type recurrent fuzzy network for dynamic plant control. Comparative evaluations with GA and PSO highlight the superior performance of HGAPSO in recurrent network design tasks.

In this paper, the extension of SiC power technology to higher voltage 10 kV/10 A SiC DMOSFETs and SiC JBS diodes is discussed. A new 10 kV/120 A SiC power module using these 10 kV SiC devices is also described which enables a compact 13.8 kV to 465/√3 solid state power substation (SSPS) rated at 1 MVA.

This paper proposes a new bridge modular switched-capacitor-based ac–ac converter, which is intended to replace conventional transformers in commercial and residential applications, especially for microgrids. The main advantages of the proposed converter lie in high efficiency and power factor, bidirectional operation, and capacitor voltage self-balancing ability. Another distinct merit is its symmetric construction, with which the voltage ripple of charge/pump capacitors can be cancelled out, and the output voltage will be smooth with significant ripple reduction. It is also noteworthy that the symmetric construction ensures that the two symmetric parts can work independently under a simplified control strategy. In addition, the modular configuration contributes to convenient voltage extension. Compared with the reported flying-capacitor switched-capacitor ac–ac converter, the proposed converter requires fewer switches and capacitors per unit of voltage conversion, thus suffering lower voltage stress. To demonstrate the performance of the converter, a prototype with 600-W 220-Vrms high-side voltage, 55-Vrms low-side voltage, and a switching frequency of 100 kHz was designed and fabricated. The relevant experimental results are reported herein. The maximum and rated power efficiency obtained were 95.9% and 94.4%, respectively.

Adapting power-electronic converters that are used in pulsed-power application attracted considerable attention during recent years. In this paper, a modular bipolar high-voltage pulse generator is proposed based on the voltage multipliers and half-bridge converters concept by using power electronics switches. This circuit is capable to generate repetitive high voltage bipolar pulses with a flexible output pattern, by using low voltage power supply and elements. The proposed topology was simulated in MATLAB/Simulink. To verify the circuit operation a four-stage laboratory prototype has been assembled and tested. The results confirm the theoretical analysis and show the validity of the converter scheme.

This paper is mainly concerned with a high frequency soft-switching PWM inverter suitable for consumer induction heating system. Proposed system is composed of soft switching chopper based voltage boost PFC input stage and phase shifted PWM controlled full bridge ZCZVS high frequency inverter stage. Its fundamentals of operating performances are illustrated and evaluated on the experimental results. Its effectiveness is substantially proved on the basis of the experimental results from a practical point of view.

A pulse-width-modulation (PWM) scheme that uses the converter switching frequency to minimize unwanted load current harmonics is described. This results in the reduction of the number of switch communications per cycle. The method is suitable for high-performance variable-speed AC-motor drives that require high-output switching frequencies for near-silent operation. It is also applicable, without change, to voltage or current-source inverters and to two and four-quadrant three-phase PWM rectifiers. Key predicted results have been verified experimentally on a 5 kVA inverter breadboard.<<ETX>>

This paper proposes an LLC resonant converter based LED (Light Emitting Diode) lamp driver with high power factor. The proposed circuit uses a boost converter for PFC (Power Factor Correction) which operates in continuous conduction mode (CCM) and a quasi half bridge resonant converter to drive the LED lamp load. The LLC converter is designed such that solid state switches of quasi half bridge are working under zero voltage switching (ZVS) to reduce switching losses. The analysis, design, modeling and simulation of 50 W LED driver are carried out using MATLAB/Simulink tool for universal AC mains. The power quality indices are calculated such as total harmonic distortion of AC mains current (THDi), power factor (PF) and crest factor (CF) to evaluate the performance of proposed LED lamp driver.

A novel magnetic coupler of large r r gap IS presented It IS developed for the eleehlo relucle s aumahc mduchve charger The ncw induchve coupler proposed hqe has sUmcient mung ~oducmnce ven rf I has a large a s gap Calculated exrung inductance 18 40 pH at 2 nuns of w d m g and 5 mm ay gap, whch apees well with measured value In order to assess the gmerahon of heat c w e d by the eddy cment, the magneuc flux densrhes in the lnduchve chaser and also a flat non plate, to wbch the inductive charger IS attached, Bre calculated The conyersion efXciency wth the coupler and a MOSFETs full-bndge inverier of IW lrHq IS 97% at 8 3 kW output

Microwaves have been widely used for the modern communication systems, which have advantages in high bit rate transmission and the easiness of compact circuit and antenna design. Gallium Nitride (GaN), featured with high breakdown and high saturation velocity, is one of the promising material for high power and high frequency devices, and a kW-class output power has already been achieved [1]. We have developed the high power and high efficiency GaN HEMTs [2–5], targeting the amplifier for the base transceiver station (BTS). This presentation summarizes our recent works, focusing on the developments for the efficiency boosting and the robustness in high power RF operation.

The design and performance of two high efficiency Ka-band power amplifier MMICs utilizing a 0.15μm GaN HEMT process technology is presented. Measured in-fixture continuous wave (CW) results for the 3-stage balanced amplifier demonstrates up to 11W of output power and 30% power added efficiency (PAE) at 30GHz. The 3-stage single-ended design produced over 6W of output power and up to 34% PAE. The die size for the balanced and single-ended MMICs are 3.24×3.60mm2 and 1.74×3.24mm2 respectively.

An eight-way waveguide-based power combiner/divider is presented and investigated in the frequency range 7.5–10.5GHz. A simple approach is proposed for design purposes. The measured combiner shows a good agreement between the simulated and measured results. Insertion loss is about −0.3 dB, return loss is less than −15 dB and isolation between adjacent output ports is better than −11 dB at 8.5GHz and reaches about −14 dB at 9.5GHz.


Question: Development of a 50-kV 100-kW three-phase resonant converter for use in a 95-GHz gyrotron system.

This document details the creation of a compact, high-repetition-rate pulsed power modulator utilizing semiconductor switches. The modulator's output parameters are customizable, with a pulse voltage range of 1-10 kV, a pulse width of 1-10 μs, a pulse repetition rate (PRR) of 1-50,000 pps, and an average output power of 10 kW. The solid-state pulsed power modulator is composed of two primary segments: an IGBT stack equipped with a gate drive circuit for generating variable width and repetition rate pulses, and a controllable voltage output capacitor charger. To achieve high voltage pulses, a simple and dependable IGBT stack configuration, inspired by the Marx generator, was devised for series connection of all storage capacitors. Furthermore, a gate driver circuit was introduced to simultaneously supply power and signals to all IGBTs, offering enhanced protection against arcs and shorts. For charging the storage capacitors, a unique 10 kW (10 kV, 1 A) high voltage capacitor charger was developed, combining the benefits of a series-loaded resonant converter and a ZVS (zero-voltage-switching) full-bridge pulse width modulation (PWM) converter, specifically tailored for the solid-state pulsed power modulator. Experimental results substantiate the effectiveness of the proposed scheme and structure for high voltage pulsed power modulators, capable of accommodating variable voltage, repetition rate, and pulse width as per the application's requirements. This design is particularly relevant for high-power applications such as the 50-kV 100-kW three-phase resonant converter for 95-GHz gyrotron development, demonstrating its potential for use in advanced pulsed power systems.

This paper presents a new control method for cascaded connected H-bridge converter based STATCOMs. These converters have been classically commutated at fundamental line-frequencies, but the evolution of power semiconductors has allowed to increase switching frequencies and the power ratings of these devices, permitting the use of PWM modulation techniques. This paper mainly focuses the DC bus voltage balancing problems, and proposes a new control technique that solves these balancing problems maintaining the delivered reactive power equally distributed among all the H-bridges of the converter

In this paper, a 1100–1350 MHz power amplifier is introduced. For the realization of the RF power amplifier an LDMOS transistor is used. The amplifier delivers 100W power to a 50 ohm load with 50% efficiency. The transducer power gain of the amplifier is has 16dB at 100W output power. A mixed lumped element and transmission line matching circuit is used to implement matching network. The simulations are performed by the nonlinear model of the transistor, which is provided by the manufacturer. Finally the results of simulations are compared with the measurements.

This paper proposes a new bridge modular switched-capacitor-based ac–ac converter, which is intended to replace conventional transformers in commercial and residential applications, especially for microgrids. The main advantages of the proposed converter lie in high efficiency and power factor, bidirectional operation, and capacitor voltage self-balancing ability. Another distinct merit is its symmetric construction, with which the voltage ripple of charge/pump capacitors can be cancelled out, and the output voltage will be smooth with significant ripple reduction. It is also noteworthy that the symmetric construction ensures that the two symmetric parts can work independently under a simplified control strategy. In addition, the modular configuration contributes to convenient voltage extension. Compared with the reported flying-capacitor switched-capacitor ac–ac converter, the proposed converter requires fewer switches and capacitors per unit of voltage conversion, thus suffering lower voltage stress. To demonstrate the performance of the converter, a prototype with 600-W 220-Vrms high-side voltage, 55-Vrms low-side voltage, and a switching frequency of 100 kHz was designed and fabricated. The relevant experimental results are reported herein. The maximum and rated power efficiency obtained were 95.9% and 94.4%, respectively.

The demand of broadband amplifier has been boosted to meet the requirements of multi-mode and multi-band wireless applications. GaN HEMT is the next generation of RF power transistor technology. In this paper, we have designed a 10W UHF broadband class-AB Power Amplifier (PA) based on GaN HEMT. The proposed amplifier has been designed and developed in the frequency range from 200-500 MHz. A maximum drain efficiency of 71% is achieved.

A High Efficiency two-stage AC/DC converter is presented and discussed in this paper, which can be applied in on-board charger for electric vehicles. The front-end converter is a high efficiency ZVS half bridge power factor correction (PFC) topology with only one device in the current flowing path. Owing to the high voltage stress of the half bridge rectifier, the SiC MOSFET is applied to reduce the conduction and switching losses and achieve high frequency. The downstream stage is a LLC resonant DC/DC converter. The peak efficiency reaches 97.5%.

This paper presents the design and measurement results of a 16-way radial power divider/combiner for solid state power amplifiers in the K band using an original assembly technique of the combined ways. The design is based on a radial non-resonant cavity connected to 8 peripheral rectangular waveguides with two waveguide-to-microstrip transitions inserted in each peripheral rectangular waveguide. Each combined way includes on the same tray the input and output waveguide-to-microstrip transitions and the MMIC amplifier location. The trays can be individually tested before mounting in the 16-way divider/combiner. The low insertion loss of the 16-way power divider/combiner leads to an estimated combining efficiency better than 86.5% from 16.5 GHz to 21 GHz with a maximum value about 88% from 17.2 to 19.2 GHz.

In this paper, a detailed design procedure is presented for a bidirectional CLLLC-type resonant converter for a battery charging application. This converter is similar to an LLC-type resonant converter with an extra inductor and capacitor in the secondary side. Soft-switching can be ensured in all switches without additional snubber or clamp circuitry. Because of soft-switching in all switches, very high-frequency operation is possible; thus, the size of the magnetics and the filter capacitors can be made small. To reduce the size and cost of the converter, a CLLC-type resonant network is derived from the original CLLLC-type resonant network. First, in this paper, an equivalent model for the bidirectional converter is derived for the steady-state analysis. Then, the design methodology is presented for the CLLLC-type resonant converter. Design of this converter includes determining the transformer turns ratio, design of the magnetizing inductance based on zero-voltage switching condition, design of the resonant inductances and capacitances. Then, the CLLCtype resonant network is derived from the CLLLC-type resonant network. To validate the design procedure, a 3.5-kW converter was designed following the guidelines in the proposed methodology. A prototype was built and tested in the laboratory. Experimental results verified the design procedure presented.

Today's distribution system is expected to supply power to loads for which it was not designed. Moreover, high penetration of distributed generation units is redefining the requirements for the design, control and operation of the electric distribution system. A Hybrid Distribution Transformer is a potential cost-effective alternative solution to various distribution grid control devices. The Hybrid Distribution Transformer is realized by augmenting a regular transformer with a fractionally rated power electronic converter, which provides the transformer with additional control capabilities. The Hybrid Distribution Transformer concept can provide dynamic ac voltage regulation, reactive power compensation and, in future designs, form an interface with energy storage devices. Other potential functionalities that can be realized from the Hybrid Distribution Transformer include voltage phase angle control, harmonic compensation and voltage sag compensation. This paper presents the concept of a Hybrid Distribution Transformer and the status of our efforts towards a 500 kVA, 12.47 kV/480 V field demonstrator.

In this paper, a comprehensive model of the variable-speed constant-frequency aircraft electric power system is developed to study the performance characteristics of the system and, in particular, the system power quality over a frequency range of operation of 400 Hz to 800 Hz. A fully controlled active power filter is designed to regulate the load terminal voltage, eliminate harmonics, correct supply power factor, and minimize the effect of unbalanced loads. The control algorithm for the active power filter (APF) is based on the perfect harmonic cancellation method which provides a three-phase reference supply current in phase with its positive-sequence fundamental voltage. The proposed APF is integrated into the model of a 90-kVA advanced aircraft electric power system under VSCF operation. The performance characteristics of the system are studied with the frequency of the generator's output voltage varied from 400 Hz to 800 Hz under different loading conditions. Several case studies are presented including dc loads as well as passive and dynamic ac loads. The power quality characteristics of the studied aircraft electric power system with the proposed active filter are shown to be in compliance with the most recent military aircraft electrical standards MIL-STD-704F as well as with the IEEE Std. 519.

A review of mainly the past two years is undertaken of the industrial applications of pulsed power. Repetitively operated pulsed power generators with a moderate peak power have been developed for industrial applications. These generators are reliable and have low maintenance. Development of the pulsed power generators helps promote industrial applications of pulsed power for such things as food processing, medical treatment, water treatment, exhaust gas treatment, ozone generation, engine ignition, ion implantation and others. Here, industrial applications of pulsed power are classified by application for biological effects, for pulsed streamer discharges in gases, for pulsed discharges in liquid or liquid- mixture, and for material processing.


Question: Residual, Inception, and Classical Networks for Low-Resolution Facial Recognition Tasks

This paper presents DeepFace, a groundbreaking approach that significantly narrows the gap between human-level performance and machine learning in the realm of face verification. The traditional pipeline for face recognition, which includes detection, alignment, representation, and classification, is re-examined, particularly focusing on the alignment and representation stages. DeepFace employs a sophisticated 3D face modeling technique to apply a piecewise affine transformation, ensuring precise alignment. The representation of the face is then derived from a deep neural network consisting of nine layers. Unlike standard convolutional layers, this network utilizes more than 120 million parameters across several locally connected layers without weight sharing. To effectively train this complex network, a massive facial dataset was assembled, comprising over four million images across more than 4,000 unique identities. The resulting learned representations, bolstered by the model-based alignment and extensive facial database, exhibit remarkable generalization capabilities, even in unconstrained environments, with a simple classifier. DeepFace achieves a remarkable 97.35% accuracy on the Labeled Faces in the Wild (LFW) dataset, marking a significant 27% reduction in error compared to previous state-of-the-art methods, and closely mirroring human-level performance.

Due to its indefinite evaluation criterion, facial beauty analysis has shown its challenges in pattern recognition and biometric recognition. Plenty of methods have been designed for solving this problem, but there are still various limitations. Most of the available research results in this field are achieved on a small-scale facial beauty database which is difficult in modeling the structure information for facial beauty. And majority of the presented facial beauty prediction algorithm requires burden landmark or expensive optimization procedure. In this paper, we have established a large scale facial beauty database named LSFBD, which is a new database for facial beauty analysis. The LSFBD database contains 20000 labeled images, which has 10000 unconstrained male subjects and 10000 unconstrained female subjects separately, and all images are verified with well-designed rating with average scores and standard deviation. In this database, manually adding the extreme beauty images make the distribution more reasonable and the result more accurate. CRBM or Eigenfaces results are presented in the paper as benchmark for evaluating the task of facial beauty prediction.

In this paper, we propose a simultaneous feature and dictionary learning (SFDL) method for image set-based face recognition, where each training and testing example contains a set of face images, which were captured from different variations of pose, illumination, expression, resolution, and motion. While a variety of feature learning and dictionary learning methods have been proposed in recent years and some of them have been successfully applied to image set-based face recognition, most of them learn features and dictionaries for facial image sets individually, which may not be powerful enough because some discriminative information for dictionary learning may be compromised in the feature learning stage if they are applied sequentially, and vice versa. To address this, we propose a SFDL method to learn discriminative features and dictionaries simultaneously from raw face pixels so that discriminative information from facial image sets can be jointly exploited by a one-stage learning procedure. To better exploit the nonlinearity of face samples from different image sets, we propose a deep SFDL (D-SFDL) method by jointly learning hierarchical non-linear transformations and class-specific dictionaries to further improve the recognition performance. Extensive experimental results on five widely used face data sets clearly shows that our SFDL and D-SFDL achieve very competitive or even better performance with the state-of-the-arts.

Parameterized models can produce realistic, manipulable images of human faces— with a surprisingly small number of parameters.

Existing person re-identification (re-id) methods rely mostly on either localised or global feature representation alone. This ignores their joint benefit and mutual complementary effects. In this work, we show the advantages of jointly learning local and global features in a Convolutional Neural Network (CNN) by aiming to discover correlated local and global features in different context. Specifically, we formulate a method for joint learning of local and global feature selection losses designed to optimise person re-id when using only generic matching metrics such as the L2 distance. We design a novel CNN architecture for Jointly Learning Multi-Loss (JLML) of local and global discriminative feature optimisation subject concurrently to the same re-id labelled information. Extensive comparative evaluations demonstrate the advantages of this new JLML model for person re-id over a wide range of state-of-the-art re-id methods on five benchmarks (VIPeR, GRID, CUHK01, CUHK03, Market-1501).

In this work, we present a simple, highly efficient and modularized Dual Path Network (DPN) for image classification which presents a new topology of connection paths internally. By revealing the equivalence of the state-of-the-art Residual Network (ResNet) and Densely Convolutional Network (DenseNet) within the HORNN framework, we find that ResNet enables feature re-usage while DenseNet enables new features exploration which are both important for learning good representations. To enjoy the benefits from both path topologies, our proposed Dual Path Network shares common features while maintaining the flexibility to explore new features through dual path architectures. Extensive experiments on three benchmark datasets, ImagNet-1k, Places365 and PASCAL VOC, clearly demonstrate superior performance of the proposed DPN over state-of-the-arts. In particular, on the ImagNet-1k dataset, a shallow DPN surpasses the best ResNeXt-101(64× 4d) with 26% smaller model size, 25% less computational cost and 8% lower memory consumption, and a deeper DPN (DPN-131) further pushes the state-of-the-art single model performance with about 2 times faster training speed. Experiments on the Places365 large-scale scene dataset, PASCAL VOC detection dataset, and PASCAL VOC segmentation dataset also demonstrate its consistently better performance than DenseNet, ResNet and the latest ResNeXt model over various applications.

Facial expression recognition has been an active research area in the past ten years, with a growing application area like avatar animation and neuromarketing. The recognition of facial expressions is not an easy problem for machine learning methods, since different people can vary in the way that they show their expressions. And even an image of the same person in one expression can vary in brightness, background and position. Therefore, facial expression recognition is still a challenging problem in computer vision. In this work, we propose a simple solution for facial expression recognition that uses a combination of standard methods, like Convolutional Network and specific image pre-processing steps. Convolutional networks, and the most machine learning methods, achieve better accuracy depending on a given feature set. Therefore, a study of some image pre-processing operations that extract only expression specific features of a face image is also presented. The experiments were carried out using a largely used public database for this problem. A study of the impact of each image pre-processing operation in the accuracy rate is presented. To the best of our knowledge, our method achieves the best result in the literature, 97.81% of accuracy, and takes less time to train than state-of-the-art methods.

We analyze the usage of Speeded Up Robust Features (SURF) as local descriptors for face recognition. The effect of different feature extraction and viewpoint consistency constrained matching approaches are analyzed. Furthermore, a RANSAC based outlier removal for system combination is proposed. The proposed approach allows to match faces under partial occlusions, and even if they are not perfectly aligned or illuminated. Current approaches are sensitive to registration errors and usually rely on a very good initial alignment and illumination of the faces to be recognized. A grid-based and dense extraction of local features in combination with a block-based matching accounting for different viewpoint constraints is proposed, as interest-point based feature extraction approaches for face recognition often fail. The proposed SURF descriptors are compared to SIFT descriptors. Experimental results on the AR-Face and CMU-PIE database using manually aligned faces, unaligned faces, and partially occluded faces show that the proposed approach is robust and can outperform current generic approaches.

Keywords: Eigenface, face Recognition, k-means clustering, principle component analysis (PCA)

Metric learning aims to construct an embedding where two extracted features corresponding to the same identity are likely to be closer than features from different identities. This paper presents a method for learning such a feature space where the cosine similarity is effectively optimized through a simple re-parametrization of the conventional softmax classification regime. At test time, the final classification layer can be stripped from the network to facilitate nearest neighbor queries on unseen individuals using the cosine similarity metric. This approach presents a simple alternative to direct metric learning objectives such as siamese networks that have required sophisticated pair or triplet sampling strategies in the past. The method is evaluated on two large-scale pedestrian re-identification datasets where competitive results are achieved overall. In particular, we achieve better generalization on the test set compared to a network trained with triplet loss.

Recent years have seen a major push for face recognition technology due to the large expansion of image sharing on social networks. In this paper, we consider the difficult task of determining parent-offspring resemblance using deep learning to answer the question "Who do I look like?" Although humans can perform this job at a rate higher than chance, it is not clear how they do it [2]. However, recent studies in anthropology [24] have determined which features tend to be the most discriminative. In this study, we aim to not only create an accurate system for resemblance detection, but bridge the gap between studies in anthropology with computer vision techniques. Further, we aim to answer two key questions: 1) Do offspring resemble their parents? and 2) Do offspring resemble one parent more than the other? We propose an algorithm that fuses the features and metrics discovered via gated autoencoders with a discriminative neural network layer that learns the optimal, or what we call genetic, features to delineate parent-offspring relationships. We further analyze the correlation between our automatically detected features and those found in anthropological studies. Meanwhile, our method outperforms the state-of-the-art in kinship verification by 3-10% depending on the relationship using specific (father-son, mother-daughter, etc.) and generic models.


Question: Residual, Inception, and Classical Networks for the task of recognizing faces at low resolutions.

This paper delves into the analysis of deep residual networks [1], a class of profoundly layered architectures that exhibit superior accuracy and favorable convergence characteristics. Our focus is on the propagation formulas underpinning the residual building blocks, which reveal that forward and backward signals can be seamlessly transmitted from one block to another, provided identity mappings are employed as skip connections and post-addition activation is utilized. A suite of ablation experiments underscores the critical role of these identity mappings. Inspired by these findings, we introduce an enhanced residual unit that facilitates easier training and enhances generalization. We showcase improved outcomes with a 1001-layer ResNet on CIFAR-10 (achieving a 4.62% error rate) and CIFAR-100, alongside a 200-layer ResNet on ImageNet. For replication and further exploration, the source code is available at: https://github.com/KaimingHe/resnet-1k-layers.

Our exploration highlights the effectiveness of identity mappings in deep residual networks, enabling efficient signal propagation and enhancing model performance. This novel insight leads to the development of an improved residual unit, demonstrating the potential for even deeper networks to achieve state-of-the-art results on challenging image datasets. The provision of the code facilitates further research and experimentation within the community, contributing to the advancement of deep learning techniques.

Traditional architectures for solving computer vision problems and the degree of success they enjoyed have been heavily reliant on hand-crafted features. However, of late, deep learning techniques have offered a compelling alternative – that of automatically learning problem-specific features. With this new paradigm, every problem in computer vision is now being re-examined from a deep learning perspective. Therefore, it has become important to understand what kind of deep networks are suitable for a given problem. Although general surveys of this fast-moving paradigm (i.e., deep-networks) exist, a survey specific to computer vision is missing. We specifically consider one form of deep networks widely used in computer vision – convolutional neural networks (CNNs). We start with “AlexNet” as our base CNN and then examine the broad variations proposed over time to suit different applications. We hope that our recipe-style survey will serve as a guide, particularly for novice practitioners intending to use deep-learning techniques for computer vision.

2D Face recognition systems bound to fail on images with varying pose angles and occlusions. Many pose invariant methods are proposed in recent years but they are still not able to achieve very good accuracies. So in order to achieve a better accuracy we need to extend algorithms over 3D faces. Due to the high cost involved in acquisition of 3D faces we developed our approach for low-cost and low-quality Microsoft Kinect Sensor and propose an algorithm to produce better results than existing 2D Face recognition techniques even after compromising on the quality of the images from the sensor. Our proposed algorithm is based on modified SURF descriptors on RGB images combined with various enhancements on automatically generated training images using Depth and Color images. We compare our results obtained with State Of The Art Techniques obtained on publicly available RGB-D Face databases. Our System obtained recognition rate of 98.07% on 30° CurtinFace Database, 89.28% on EURECOM Database, 98.00% on 15° Internal Database and 81.00% on 30° Internal Database.

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.

In this paper, we explore the possibility of leveraging Residual Networks (ResNet), a powerful structure in constructing extremely deep neural network for image understanding, to improve recurrent neural networks (RNN) for modeling sequential data. We show that for sequence classification tasks, incorporating residual connections into recurrent structures yields similar accuracy to Long Short Term Memory (LSTM) RNN with much fewer model parameters. In addition, we propose two novel models which combine the best of both residual learning and LSTM. Experiments show that the new models significantly outperform LSTM.

We propose a modeland exemplar-based approach for face recognition. This problem has been previously tackled using either models or exemplars, with limited success. Our idea uses models to synthesize many more exemplars, which are then used in the learning stage of a face recognition system. To demonstrate this, we develop a statistical shape-fromshading model to recover face shape from a single image, and to synthesize the same face under new illumination. We then use this to build a simple and fast classifier that was not possible before because of a lack of training data.

Facial recognition is a challenging problem in image processing and machine learning areas. Since widespread applications of facial recognition make it a valuable research topic, this work tries to develop some new facial recognition systems that have both high recognition accuracy and fast running speed. Efforts are made to design facial recognition systems by combining different algorithms. Comparisons and evaluations of recognition accuracy and running speed show that PCA + SVM achieves the best recognition result, which is over 95% for certain training data and eigenface sizes. Also, PCA + KNN achieves the balance between recognition accuracy and running speed.

An expression-invariant 3D face recognition approach is presented. Our basic assumption is that facial expressions can be modelled as isometries of the facial surface. This allows to construct expression-invariant representations of faces using the bending-invariant canonical forms approach. The result is an efficient and accurate face recognition algorithm, robust to facial expressions, that can distinguish between identical twins (the first two authors). We demonstrate a prototype system based on the proposed algorithm and compare its performance to classical face recognition methods. The numerical methods employed by our approach do not require the facial surface explicitly. The surface gradients field, or the surface metric, are sufficient for constructing the expression-invariant representation of any given face. It allows us to perform the 3D face recognition task while avoiding the surface reconstruction stage.

Multimodal recognition of affective states is a difficult problem, unless the recording conditions are carefully controlled. For recognition “in the wild”, large variances in face pose and illumination, cluttered backgrounds, occlusions, audio and video noise, as well as issues with subtle cues of expression are some of the issues to target. In this paper, we describe a multimodal approach for video-based emotion recognition in the wild. We propose using summarizing functionals of complementary visual descriptors for video modeling. These features include deep convolutional neural network (CNN) based features obtained via transfer learning, for which we illustrate the importance of flexible registration and fine-tuning. Our approach combines audio and visual features with least squares regression based classifiers and weighted score level fusion. We report state-of-the-art results on the EmotiW Challenge for “in the wild” facial expression recognition. Our approach scales to other problems, and ranked top in the ChaLearn-LAP First Impressions Challenge 2016 from video clips collected in the wild.

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.

We propose a novel end-to-end deep architecture for face landmark detection, based on a deep convolutional and deconvolutional network followed by carefully designed recurrent network structures. The pipeline of this architecture consists of three parts. Through the first part, we encode an input face image to resolution-preserved deconvolutional feature maps via a deep network with stacked convolutional and deconvolutional layers. Then, in the second part, we estimate the initial coordinates of the facial key points by an additional convolutional layer on top of these deconvolutional feature maps. In the last part, by using the deconvolutional feature maps and the initial facial key points as input, we refine the coordinates of the facial key points by a recurrent network that consists of multiple long short-term memory components. Extensive evaluations on several benchmark data sets show that the proposed deep architecture has superior performance against the state-of-the-art methods.


Question: Residual, Inception, and Classical Networks for Low-Resolution Facial Recognition Tasks

In this research, we introduce an innovative and effective facial image representation technique utilizing local binary pattern (LBP) texture features for enhanced face recognition accuracy. The methodology involves segmenting the facial image into multiple regions, from which the LBP feature distributions are meticulously extracted. These distributions are then concatenated to form an enriched feature vector, serving as a comprehensive face descriptor. The efficacy of our proposed method is rigorously evaluated in the context of face recognition under various challenging conditions. Additionally, potential applications and further extensions of this technique are explored, showcasing its versatility and robustness in the field of facial recognition technology.

We propose a novel approach for generating region proposals for performing face-detection. Instead of classifying anchor boxes using features from a pixel in the convolutional feature map, we adopt a pooling-based approach for generating region proposals. However, pooling hundreds of thousands of anchors which are evaluated for generating proposals becomes a computational bottleneck during inference. To this end, an efficient anchor placement strategy for reducing the number of anchor-boxes is proposed. We then show that proposals generated by our network (Floating Anchor Region Proposal Network, FA-RPN) are better than RPN for generating region proposals for face detection. We discuss several beneficial features of FA-RPN proposals like iterative refinement, placement of fractional anchors and changing anchors which can be enabled without making any changes to the trained model. Our face detector based on FA-RPN obtains 89.4% mAP with a ResNet-50 backbone on the WIDER dataset.

Face recognition systems play a vital role in many applications including surveillance, biometrics and security. In this work, we present a {\textit complete} real-time face recognition system consisting of a face detection, a recognition and a down sampling module using an FPGA. Our system provides an end-to-end solution for face recognition, it receives video input from a camera, detects the locations of the face(s) using the Viola-Jones algorithm, subsequently recognizes each face using the Eigenface algorithm, and outputs the results to a display. Experimental results show that our complete face recognition system operates at 45 frames per second on a Virtex-5 FPGA.

We outline a design for a FACS-based facial expression recognition system and describe in more detail the implementation of two of its main components. Firstly we look at how features that are useful from a pattern analysis point of view can be extracted from a raw input image. We show that good results can be obtained by using the method of local binary patterns (LPB) to generate a large number of candidate features and then selecting from them using fast correlation-based filtering (FCBF). Secondly we show how Platt scaling can be used to improve the performance of an error-correcting output code (ECOC) classifier.

Existing person re-identification (re-id) methods rely mostly on either localised or global feature representation alone. This ignores their joint benefit and mutual complementary effects. In this work, we show the advantages of jointly learning local and global features in a Convolutional Neural Network (CNN) by aiming to discover correlated local and global features in different context. Specifically, we formulate a method for joint learning of local and global feature selection losses designed to optimise person re-id when using only generic matching metrics such as the L2 distance. We design a novel CNN architecture for Jointly Learning Multi-Loss (JLML) of local and global discriminative feature optimisation subject concurrently to the same re-id labelled information. Extensive comparative evaluations demonstrate the advantages of this new JLML model for person re-id over a wide range of state-of-the-art re-id methods on five benchmarks (VIPeR, GRID, CUHK01, CUHK03, Market-1501).

In this paper, we introduce the DEEP-SEE FACE framework, an assistive device designed to improve cognition, interaction, and communication of visually impaired (VI) people in social encounters. The proposed approach jointly exploits computer vision algorithms (region proposal networks, ATLAS tracking and global, and low-level image descriptors) and deep convolutional neural networks in order to detect, track, and recognize, in real-time, various persons existent in the video streams. The major contribution of the paper concerns a global, fixed-size face representation that takes into the account of various video frames while remaining independent of the length of the image sequence. To this purpose, we introduce an effective weight adaptation scheme that is able to determine the relevance assigned to each face instance, depending on the frame degree of motion/camera blur, scale variation, and compression artifacts. Another relevant contribution involves a hard negative mining stage that helps us differentiating between known and unknown face identities. The experimental results, carried out on a large-scale data set, validate the proposed methodology with an average accuracy and recognition rates superior to 92%. When tested in real life, indoor/outdoor scenarios, the DEEP-SEE FACE prototype proves to be effective and easy to use, allowing the VI people to access visual information during social events.

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.

Compressive Sensing has become one of the standard methods of face recognition within the literature. We show, however, that the sparsity assumption which underpins much of this work is not supported by the data. This lack of sparsity in the data means that compressive sensing approach cannot be guaranteed to recover the exact signal, and therefore that sparse approximations may not deliver the robustness or performance desired. In this vein we show that a simple £2 approach to the face recognition problem is not only significantly more accurate than the state-of-the-art approach, it is also more robust, and much faster. These results are demonstrated on the publicly available YaleB and AR face datasets but have implications for the application of Compressive Sensing more broadly.

We present a novel discriminative regression based approach for the Constrained Local Models (CLMs) framework, referred to as the Discriminative Response Map Fitting (DRMF) method, which shows impressive performance in the generic face fitting scenario. The motivation behind this approach is that, unlike the holistic texture based features used in the discriminative AAM approaches, the response map can be represented by a small set of parameters and these parameters can be very efficiently used for reconstructing unseen response maps. Furthermore, we show that by adopting very simple off-the-shelf regression techniques, it is possible to learn robust functions from response maps to the shape parameters updates. The experiments, conducted on Multi-PIE, XM2VTS and LFPW database, show that the proposed DRMF method outperforms state-of-the-art algorithms for the task of generic face fitting. Moreover, the DRMF method is computationally very efficient and is real-time capable. The current MATLAB implementation takes 1 second per image. To facilitate future comparisons, we release the MATLAB code and the pre-trained models for research purposes.

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.

This study introduces a novel conditional recycle generative adversarial network for facial attribute transformation, which can transform high-level semantic face attributes without changing the identity. In our approach, we input a source facial image to the conditional generator with target attribute condition to generate a face with the target attribute. Then we recycle the generated face back to the same conditional generator with source attribute condition. A face which should be similar to that of the source face in personal identity and facial attributes is generated. Hence, we introduce a recycle reconstruction loss to enforce the final generated facial image and the source facial image to be identical. Evaluations on the CelebA dataset demonstrate the effectiveness of our approach. Qualitative results show that our approach can learn and generate high-quality identity-preserving facial images with specified attributes.


Question: Residual, Inception, and Classical Networks for Low-Resolution Facial Recognition Tasks

This paper presents a novel method for face recognition in low-resolution video data, a critical requirement for security and safety applications like surveillance and forensics. The proposed approach utilizes a Convolutional Neural Network (CNN) specifically tailored for low-resolution environments, combined with a manifold-based track comparison strategy. To address the challenges posed by low-resolution images, the CNN's architecture is modified to avoid bottlenecks and excessive upscaling of facial images. Training the network involves a large-scale dataset, comprising both self-collected video face data and publicly available image face datasets, totaling approximately 1.4 million images. To manage extensive video data and optimize comparison, the CNN-generated face descriptors are efficiently compared at the track level using local patch means. The effectiveness of the proposed method is demonstrated by achieving an accuracy of 80.3 percent on a 32x32 pixels low-resolution variant of the YouTube Faces Database. This performance surpasses that of local image descriptors and the state-of-the-art VGG-Face network [20] in the low-resolution domain. The method's superiority is further validated through testing on a self-collected in-the-wild surveillance dataset.

This paper presents the implementation details of the proposed solution to the Emotion Recognition in the Wild 2016 Challenge, in the category of video-based emotion recognition. The proposed approach takes the video stream from the audio-video trimmed clips provided by the challenge as input and produces the emotion label corresponding to this video sequence. This output is encoded as one out of seven classes: the six basic emotions (Anger, Disgust, Fear, Happiness, Sad, Surprise) and Neutral. Overall, the system consists of several pipelined modules: face detection, image pre-processing, deep feature extraction, feature encoding and, finally, an SVM classification.
 This system achieves 59.42% validation accuracy, surpassing the competition baseline of 38.81%. With regard to test data, our system achieves 56.66% recognition rate, also improving the competition baseline of 40.47%.

Most current approaches to recognition aim to be scaleinvariant. However, the cues available for recognizing a 300 pixel tall object are qualitatively different from those for recognizing a 3 pixel tall object. We argue that for sensors with finite resolution, one should instead use scale-variant, or multiresolution representations that adapt in complexity to the size of a putative detection window. We describe a multiresolution model that acts as a deformable part-based model when scoring large instances and a rigid template with scoring small instances. We also examine the interplay of resolution and context, and demonstrate that context is most helpful for detecting low-resolution instances when local models are limited in discriminative power. We demonstrate impressive results on the Caltech Pedestrian benchmark, which contains object instances at a wide range of scales. Whereas recent state-of-theart methods demonstrate missed detection rates of 86%-37% at 1 falsepositive-per-image, our multiresolution model reduces the rate to 29%.

In this work, we present a simple, highly efficient and modularized Dual Path Network (DPN) for image classification which presents a new topology of connection paths internally. By revealing the equivalence of the state-of-the-art Residual Network (ResNet) and Densely Convolutional Network (DenseNet) within the HORNN framework, we find that ResNet enables feature re-usage while DenseNet enables new features exploration which are both important for learning good representations. To enjoy the benefits from both path topologies, our proposed Dual Path Network shares common features while maintaining the flexibility to explore new features through dual path architectures. Extensive experiments on three benchmark datasets, ImagNet-1k, Places365 and PASCAL VOC, clearly demonstrate superior performance of the proposed DPN over state-of-the-arts. In particular, on the ImagNet-1k dataset, a shallow DPN surpasses the best ResNeXt-101(64× 4d) with 26% smaller model size, 25% less computational cost and 8% lower memory consumption, and a deeper DPN (DPN-131) further pushes the state-of-the-art single model performance with about 2 times faster training speed. Experiments on the Places365 large-scale scene dataset, PASCAL VOC detection dataset, and PASCAL VOC segmentation dataset also demonstrate its consistently better performance than DenseNet, ResNet and the latest ResNeXt model over various applications.

Interpersonal relation defines the association, e.g., warm, friendliness, and dominance, between two or more people. We investigate if such fine-grained and high-level relation traits can be characterized and quantified from face images in the wild. We address this challenging problem by first studying a deep network architecture for robust recognition of facial expressions. Unlike existing models that typically learn from facial expression labels alone, we devise an effective multitask network that is capable of learning from rich auxiliary attributes such as gender, age, and head pose, beyond just facial expression data. While conventional supervised training requires datasets with complete labels (e.g., all samples must be labeled with gender, age, and expression), we show that this requirement can be relaxed via a novel attribute propagation method. The approach further allows us to leverage the inherent correspondences between heterogeneous attribute sources despite the disparate distributions of different datasets. With the network we demonstrate state-of-the-art results on existing facial expression recognition benchmarks. To predict inter-personal relation, we use the expression recognition network as branches for a Siamese model. Extensive experiments show that our model is capable of mining mutual context of faces for accurate fine-grained interpersonal prediction.

Here, we develop an audiovisual deep residual network for multimodal apparent personality trait recognition. The network is trained end-to-end for predicting the Big Five personality traits of people from their videos. That is, the network does not require any feature engineering or visual analysis such as face detection, face landmark alignment or facial expression recognition. Recently, the network won the third place in the ChaLearn First Impressions Challenge with a test accuracy of 0.9109.

Keywords: Eigenface, face Recognition, k-means clustering, principle component analysis (PCA)

We analyze the usage of Speeded Up Robust Features (SURF) as local descriptors for face recognition. The effect of different feature extraction and viewpoint consistency constrained matching approaches are analyzed. Furthermore, a RANSAC based outlier removal for system combination is proposed. The proposed approach allows to match faces under partial occlusions, and even if they are not perfectly aligned or illuminated. Current approaches are sensitive to registration errors and usually rely on a very good initial alignment and illumination of the faces to be recognized. A grid-based and dense extraction of local features in combination with a block-based matching accounting for different viewpoint constraints is proposed, as interest-point based feature extraction approaches for face recognition often fail. The proposed SURF descriptors are compared to SIFT descriptors. Experimental results on the AR-Face and CMU-PIE database using manually aligned faces, unaligned faces, and partially occluded faces show that the proposed approach is robust and can outperform current generic approaches.

Person re-identification (ReID) is an important task in wide area video surveillance which focuses on identifying people across different cameras. Recently, deep learning networks with a triplet loss become a common framework for person ReID. However, the triplet loss pays main attentions on obtaining correct orders on the training set. It still suffers from a weaker generalization capability from the training set to the testing set, thus resulting in inferior performance. In this paper, we design a quadruplet loss, which can lead to the model output with a larger inter-class variation and a smaller intra-class variation compared to the triplet loss. As a result, our model has a better generalization ability and can achieve a higher performance on the testing set. In particular, a quadruplet deep network using a margin-based online hard negative mining is proposed based on the quadruplet loss for the person ReID. In extensive experiments, the proposed network outperforms most of the state-of-the-art algorithms on representative datasets which clearly demonstrates the effectiveness of our proposed method.

In this paper, we propose a simple and general framework for training very tiny CNNs (e.g. VGG with the number of channels reduced to 1 32 ) for object detection. Due to limited representation ability, it is challenging to train very tiny networks for complicated tasks like detection. To the best of our knowledge, our method, called Quantization Mimic, is the first one focusing on very tiny networks. We utilize two types of acceleration methods: mimic and quantization. Mimic improves the performance of a student network by transfering knowledge from a teacher network. Quantization converts a full-precision network to a quantized one without large degradation of performance. If the teacher network is quantized, the search scope of the student network will be smaller. Using this feature of the quantization, we propose Quantization Mimic. It first quantizes the large network, then mimic a quantized small network. The quantization operation can help student network to better match the feature maps from teacher network. To evaluate our approach, we carry out experiments on various popular CNNs including VGG and Resnet, as well as different detection frameworks including Faster R-CNN and R-FCN. Experiments on Pascal VOC and WIDER FACE verify that our Quantization Mimic algorithm can be applied on various settings and outperforms state-of-the-art model acceleration methods given limited computing resouces.

Recent work demonstrated that computer graphics techniques can be used to improve face recognition performances by synthesizing multiple new views of faces available in existing face collections. By so doing, more images and more appearance variations are available for training, thereby improving the deep models trained on these images. Similar rendering techniques were also applied at test time to align faces in 3D and reduce appearance variations when comparing faces. These previous results, however, did not consider the computational cost of rendering: At training, rendering millions of face images can be prohibitive; at test time, rendering can quickly become a bottleneck, particularly when multiple images represent a subject. This paper builds on a number of observations which, under certain circumstances, allow rendering new 3D views of faces at a computational cost which is equivalent to simple 2D image warping. We demonstrate this by showing that the run-time of an optimized OpenGL rendering engine is slower than the simple Python implementation we designed for the same purpose. The proposed rendering is used in a face recognition pipeline and tested on the challenging IJB-A and Janus CS2 benchmarks. Our results show that our rendering is not only fast, but improves recognition accuracy.


Question: Residual, Inception, and Classical Networks for the task of recognizing faces at low resolutions.

Caffe: A Framework for High-Performance Deep Learning

Caffe, designed by the Berkeley Vision and Learning Center (BVLC) with active community contributions, is a powerful and adaptable framework for deep learning algorithms and reference models. This BSD-licensed C++ library, complemented by Python and MATLAB bindings, facilitates the efficient training and deployment of convolutional neural networks and other sophisticated deep learning models on standard hardware configurations. Leveraging CUDA GPU computation, Caffe demonstrates its prowess in handling massive datasets, capable of processing over 40 million images daily on a single K40 or Titan GPU, with an impressive processing speed of approximately 2 milliseconds per image. This makes it an ideal solution for industry and internet-scale media requirements.

The framework's design philosophy hinges on the separation of model representation from its implementation, enabling seamless experimentation and platform switching. This feature ensures a smooth transition from development to deployment, whether on prototyping machines or in cloud environments. Caffe's versatility and robustness have made it a cornerstone for ongoing research initiatives, large-scale industrial applications, and startup innovations in the domains of vision, speech, and multimedia, showcasing its critical role in advancing these fields.

Deep residual networks were shown to be able to scale up to thousands of layers and still have improving performance. However, each fraction of a percent of improved accuracy costs nearly doubling the number of layers, and so training very deep residual networks has a problem of diminishing feature reuse, which makes these networks very slow to train. To tackle these problems, in this paper we conduct a detailed experimental study on the architecture of ResNet blocks, based on which we propose a novel architecture where we decrease depth and increase width of residual networks. We call the resulting network structures wide residual networks (WRNs) and show that these are far superior over their commonly used thin and very deep counterparts. For example, we demonstrate that even a simple 16-layer-deep wide residual network outperforms in accuracy and efficiency all previous deep residual networks, including thousand-layerdeep networks, achieving new state-of-the-art results on CIFAR, SVHN, COCO, and significant improvements on ImageNet. Our code and models are available at https: //github.com/szagoruyko/wide-residual-networks.

In past years, deep convolutional neural networks (DCNN) have achieved big successes in image classification and object detection, as demonstrated on ImageNet in academic field. However, There are some unique practical challenges remain for real-world image recognition applications, e.g., small size of the objects, imbalanced data distributions, limited labeled data samples, etc. In this work, we are making efforts to deal with these challenges through a computational framework by incorporating latest developments in deep learning. In terms of two-stage detection scheme, pseudo labeling, data augmentation, cross-validation and ensemble learning, the proposed framework aims to achieve better performances for practical image recognition applications as compared to using standard deep learning methods. The proposed framework has recently been deployed as the key kernel for several image recognition competitions organized by Kaggle. The performance is promising as our final private scores were ranked 4 out of 2293 teams for fish recognition on the challenge "The Nature Conservancy Fisheries Monitoring" and 3 out of 834 teams for cervix recognition on the challenge "Intel &MobileODT Cervical Cancer Screening", and several others. We believe that by sharing the solutions, we can further promote the applications of deep learning techniques.

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.

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.

In this work we describe experiments with eigenfaces for recognition and interactive search in a large-scale face database. Accurate visual recognition is demonstrated using a database of O(10) faces. The problem of recognition under general viewing orientation is also examined. A view-based multiple-observer eigenspace technique is proposed for use in face recognition under variable pose. In addition, a modular eigenspace description technique is used which incorporates salient features such as the eyes, nose and mouth, in an eigenfeature layer. This modular representation yields higher recognition rates as well as a more robust framework for face recognition. An automatic feature extraction technique using feature eigentemplates is also demonstrated.

2D Face recognition systems bound to fail on images with varying pose angles and occlusions. Many pose invariant methods are proposed in recent years but they are still not able to achieve very good accuracies. So in order to achieve a better accuracy we need to extend algorithms over 3D faces. Due to the high cost involved in acquisition of 3D faces we developed our approach for low-cost and low-quality Microsoft Kinect Sensor and propose an algorithm to produce better results than existing 2D Face recognition techniques even after compromising on the quality of the images from the sensor. Our proposed algorithm is based on modified SURF descriptors on RGB images combined with various enhancements on automatically generated training images using Depth and Color images. We compare our results obtained with State Of The Art Techniques obtained on publicly available RGB-D Face databases. Our System obtained recognition rate of 98.07% on 30° CurtinFace Database, 89.28% on EURECOM Database, 98.00% on 15° Internal Database and 81.00% on 30° Internal Database.

Face alignment has witnessed substantial progress in the last decade. One of the recent focuses has been aligning a dense 3D face shape to face images with large head poses. The dominant technology used is based on the cascade of regressors, e.g., CNNs, which has shown promising results. Nonetheless, the cascade of CNNs suffers from several drawbacks, e.g., lack of end-to-end training, handcrafted features and slow training speed. To address these issues, we propose a new layer, named visualization layer, which can be integrated into the CNN architecture and enables joint optimization with different loss functions. Extensive evaluation of the proposed method on multiple datasets demonstrates state-of-the-art accuracy, while reducing the training time by more than half compared to the typical cascade of CNNs. In addition, we compare across multiple CNN architectures, all with the visualization layer, to further demonstrate the advantage of its utilization.

Humans detect and identify faces in a scene with little or no effort . However, building an automated system that accomplishes this task is very difficult . There are several related Subproblems : detection of a pattern as a face . identification of the face, analysis of facial expressions, and classification based on physical features of the face . A system that performs these operations will find many applications, e .g . criminal identification, authentication in secure systems, etc . Most of the work to date has been in identification . This paper surveys the past work in solving these problems . The capability of the human visual system with respect to these problems is also discussed . It is meant to serve as a guide for an automated system . Some new approaches to these problems are also briefly discussed . Face detection Face identification Facial expressions Classification Facial features

In recent years, deep learning algorithm has been one of the most used method in machine learning. Success rate of the most popular machine learning problems has been increased by using it. In this work, we develop an eye detection method by using a deep neural network. The designed network, which is accepted as an input by Caffe, has 3 convolution layers and 3 max pooling layers. This model has been trained with 16K positive and 52K negative image patches. The last layer of the network is the classification layer which operates a softmax algorithm. The trained model has been tested with images, which were provided on FDDB and CACD datasets, and also compared with Haar eye detection algorithm. Recall value of the method is higher than the Haar algorithm on the two datasets. However, according to the precision rate, the Haar algorithm is successful on FDDB dataset.

In 2007, Labeled Faces in the Wild was released in an effort to spur research in face recognition, specifically for the problem of face verification with unconstrained images. Since that time, more than 50 papers have been published that improve upon this benchmark in some respect. A remarkably wide variety of innovative methods have been developed to overcome the challenges presented in this database. As performance on some aspects of the benchmark approaches 100% accuracy, it seems appropriate to review this progress, derive what general principles we can from these works, and identify key future challenges in face recognition. In this survey, we review the contributions to LFW for which the authors have provided results to the curators (results found on the LFW results web page). We also review the cross cutting topic of alignment and how it is used in various methods. We end with a brief discussion of recent databases designed to challenge the next generation of face recognition algorithms. Erik Learned-Miller University of Massachusetts, Amherst, Massachusetts, e-mail: elm@cs.umass.edu Gary B. Huang Howard Hughes Medical Institute, Janelia Research Campus, e-mail: gbhuang@cs.umass.edu Aruni RoyChowdhury University of Massachusetts, Amherst, Massachusetts, e-mail: aruni@cs.umass.edu Haoxiang Li Stevens Institute of Technology, Hoboken, New Jersey, e-mail: hli18@stevens.edu Gang Hua Stevens Institute of Technology, Hoboken, New Jersey, e-mail: ganghua@gmail.com


Question: Investigating location popularity within the Foursquare platform.

This paper introduces a framework named Route Inference based on Collective Knowledge (RICK) that aims to construct popular routes from uncertain trajectories generated by location-acquisition technologies. Due to the low or irregular frequency of these technologies, the routes between two consecutive points often remain uncertain. RICK addresses this issue by aggregating such uncertain trajectories to construct the top-k popular routes that sequentially connect given locations within a specified time span. The framework's approach, which can be summarized as uncertain + uncertain → certain, enhances route clarity through mutual reinforcement among uncertain trajectories. RICK is designed to support various applications, including trip planning, traffic management, and animal movement studies. It consists of two main components: routable graph construction and route inference. Initially, RICK analyzes the spatial and temporal characteristics of uncertain trajectories to build a routable graph through collaborative learning. Subsequently, leveraging the constructed routable graph, a routing algorithm is employed to identify the top-k routes according to user-defined criteria. The efficacy and efficiency of RICK have been validated through comprehensive experiments on real datasets, specifically Foursquare check-in data and taxi trajectories. The outcomes confirm RICK's capability to construct accurate and popular routes from uncertain data, enhancing the utility of location-acquisition technologies in various domains.

Social reporting is based on the idea that the members of a location-based social network observe real-world events and publish reports about their observations. Application scenarios include crisis management, bird watching or even some sorts of mobile games. A major issue in social reporting is the quality of the reports. We propose an approach to the quality problem that is based on the reciprocal confirmation of reports by other reports. This contrasts with approaches that require users to verify reports, that is, to explicitly evaluate their veridicality. We propose to use spatio-termporal proximity as a first criterion for confirmation and social distance as a second one. By combining these two measures we construct a graph containing the reports as nodes connected by confirmation edges that can adopt positive as well as negative values. This graph builds the basis for the computation of confirmation values for individual reports by different aggregation measures. By applying our approach to two use cases, we show the importance of a weighted combination, since the meaningfulness of the constituent measures varies between different contexts.

With the development of GPS and the popularity of smart phones and wearable devices, users can easily log their daily trajectories. Prior works have elaborated on mining trajectory patterns from raw trajectories. Trajectory patterns consist of hot regions and the sequential relationships among them, where hot regions refer the spatial regions with a higher density of data points. Note that some hot regions do not have any meaning for users. Moreover, trajectory patterns do not have explicit time information or semantic information. To enrich trajectory patterns, we propose semantic trajectory patterns which are referred to as the moving patterns with spatial, temporal, and semantic attributes. Given a user trajectory, we aim at mining frequent semantic trajectory patterns. Explicitly, we extract the three attributes from a raw trajectory, and convert it into a semantic mobility sequence. Given such a semantic mobility sequence, we propose two algorithms to discover frequent semantic trajectory patterns. The first algorithm, MB (standing for matching-based algorithm), is a naive method to find frequent semantic trajectory patterns. It generates all possible patterns and extracts the occurrence of the patterns from the semantic mobility sequence. The second algorithm, PS (standing for PrefixSpan-based algorithm), is developed to efficiently mine semantic trajectory patterns. Due to the good efficiency of PrefixSpan, algorithm PS will fully utilize the advantage of PrefixSpan. Since the semantic mobility sequence contains three attributes, we need to further transform it into a raw sequence before using algorithm PrefixSpan. Therefore, we propose the SS algorithm (standing for sequence symbolization algorithm) to achieve this purpose. To evaluate our proposed algorithms, we conducted experiments on the real datasets of Google Location History, and the experimental results show the effectiveness and efficiency of our proposed algorithms.

Given the abundance of online information available to mobile users, particularly tourists and weekend travelers, recommender systems that effectively filter this information and suggest interesting participatory opportunities will become increasingly important. Previous work has explored recommending interesting locations; however, users would also benefit from recommendations for activities in which to participate at those locations along with suitable times and days. Thus, systems that provide collaborative recommendations involving multiple dimensions such as location, activities and time would enhance the overall experience of users.The relationship among these dimensions can be modeled by higher-order matrices called tensors which are then solved by tensor factorization. However, these tensors can be extremely sparse. In this paper, we present a system and an approach for performing multi-dimensional collaborative recommendations for Who (User), What (Activity), When (Time) and Where (Location), using tensor factorization on sparse user-generated data. We formulate an objective function which simultaneously factorizes coupled tensors and matrices constructed from heterogeneous data sources. We evaluate our system and approach on large-scale real world data sets consisting of 588,000 Flickr photos collected from three major metro regions in USA. We compare our approach with several state-of-the-art baselines and demonstrate that it outperforms all of them.

This paper presents an analysis of continuous cellular tower data representing five months of movement from 215 randomly sampled subjects in a major urban city. We demonstrate the potential of existing community detection methodologies to identify salient locations based on the network generated by tower transitions. The tower groupings from these unsupervised clustering techniques are subsequently validated using data from Bluetooth beacons placed in the homes of the subjects. We then use these inferred locations as states within several dynamic Bayesian networks to predict each subject’s subsequent movements with over 90% accuracy. We also introduce the X-Factor model, a DBN with a latent variable corresponding to abnormal behavior. We conclude with a description of extensions for this model, such as incorporating additional contextual and temporal variables already being logged by the

There is a relationship between what we say and where we say it. Word embeddings are usually trained assuming that semantically-similar words occur within the same textual contexts. We investigate the extent to which semantically-similar words occur within the same geospatial contexts. We enrich a corpus of geolocated Twitter posts with physical data derived from Google Places and OpenStreetMap, and train word embeddings using the resulting geospatial contexts. Intrinsic evaluation of the resulting vectors shows that geographic context alone does provide useful information about semantic relatedness.

We propose a method for embedding twodimensional locations in a continuous vector space using a neural network-based model incorporating mixtures of Gaussian distributions, presenting two model variants for text-based geolocation and lexical dialectology. Evaluated over Twitter data, the proposed model outperforms conventional regression-based geolocation and provides a better estimate of uncertainty. We also show the effectiveness of the representation for predicting words from location in lexical dialectology, and evaluate it using the DARE dataset.

Increasingly in the 21st century, our daily lives leave behind a detailed digital record: our shifting thoughts and opinions shared on Twitter, our social relationships, our purchasing habits, our information seeking, our photos and videos—even the movements of our bodies and cars. Naturally, for those interested in human behavior, this bounty of personal data is irresistible. Decision makers of all kinds, from company executives to government agencies to researchers and scientists, would like to base their decisions and actions on this data. In response, a new discipline of big data analytics is forming. Fundamentally, big data analytics is a workflow that distills terabytes of low-value data (e.g., every tweet) down to, in some cases, a single bit of high-value data (Should Company X acquire Company Y? Can we reject the null hypothesis?). The goal is to see the big picture from the minutia of our digital lives. It is no surprise today that big data is useful for HCI researchers and user interface design. As one example, A/B testing is a standard practice in the usability community to help determine relative differences in user performance using different interfaces. For many years, we have used strict laboratory conditions to evaluate interfaces, but more recently we have seen the ability to implement those tests quickly and on a large population by running controlled Interactions with Big Data Analytics

Mobile contexts of use vary a lot, and may even be continuously changing during use. The context is much more than location, but its other elements are still difficult to identify or measure. Location information is becoming an integral part of different mobile devices. Current mobile services can be enhanced with location-aware features, thus providing the user with a smooth transition towards context-aware services. Potential application fields can be found in areas such as travel information, shopping, entertainment, event information and different mobile professions. This paper studies location-aware mobile services from the user's point of view. The paper draws conclusions about key issues related to user needs, based on user interviews, laboratory and field evaluations with users, and expert evaluations of location-aware services. The user needs are presented under five main themes: topical and comprehensive contents, smooth user interaction, personal and user-generated contents, seamless service entities and privacy issues.

The technology for determining the geographic location of cell phones and other hand-held devices is becoming increasingly available. It is opening the way to a wide range of applications, collectively referred to as Location Based Services (LBS), that are primarily aimed at individual users. However, if deployed to retrieve aggregated data in cities, LBS could become a powerful tool for urban analysis. This paper aims to review and introduce the potential of this technology to the urban planning community. In addition, it presents the ‘Mobile Landscapes’ project: an application in the metropolitan area of Milan, Italy, based on the geographical mapping of cell phone usage at different times of the day. The results enable a graphic representation of the intensity of urban activities and their evolution through space and time. Finally, a number of future applications are discussed and their potential for urban studies and planning is assessed.

Topic modeling has traditionally been studied for single text collections and applied to social media data represented in the form of text documents. With the emergence of many social media platforms, users find themselves using different social media for posting content and for social interaction. While many topics may be shared across social media platforms, users typically show preferences of certain social media platform(s) over others for certain topics. Such platform preferences may even be found at the individual level. To model social media topics as well as platform preferences of users, we propose a new topic model known as MultiPlatform-LDA (MultiLDA). Instead of just merging all posts from different social media platforms into a single text collection, MultiLDA keeps one text collection for each social media platform but allowing these platforms to share a common set of topics. MultiLDA further learns the user-specific platform preferences for each topic. We evaluate MultiLDA against TwitterLDA, the state-of-the-art method for social media content modeling, on two aspects: (i) the effectiveness in modeling topics across social media platforms, and (ii) the ability to predict platform choices for each post. We conduct experiments on three real-world datasets from Twitter, Instagram and Tumblr sharing a set of common users. Our experiments results show that the MultiLDA outperforms in both topic modeling and platform choice prediction tasks. We also show empirically that among the three social media platforms, “Daily matters” and “Relationship matters” are dominant topics in Twitter, “Social gathering”, “Outing” and “Fashion” are dominant topics in Instagram, and “Music”, “Entertainment” and “Fashion” are dominant topics in Tumblr.