Face Verification Research Articles

A kernel-based weight decorrelation for regularizing CNNs

Recent years has witnessed the success of convolutional neural networks (CNNs) in many machine learning and pattern recognition applications, especially in image recognition. However, due to the increasing model complexity, the parameter redundancy problem arises, and greatly degrades the performance of CNNs. To alleviate this problem, various regularization techniques, such as Dropout, have been proposed and proved their effectiveness. In this paper, we propose a novel adaptive kernel-based weight decorrelation (AKWD) framework, in order to regularize CNNs for better generalization. Different from existing works, the correlation between paring weights is measured by the cosine distance defined in RKHS associated with a specific kernel. The case with the well-known Gaussian kernel is investigated in detail, where the bandwidth parameter is adaptively estimated. By regularizing CNN models of different capacities using AKWD, better performance is achieved on several benchmark databases for both object classification and face verification tasks. In particular, when Dropout or BatchNorm is present, even higher improvements are obtained using the proposed AKWD, that demonstrates a good compatibility of the proposed regularizer with other regularization techniques.

A Face Recognition Algorithm Based on Dual-Channel Images and VGG-cut Model

In the embedded system environment, both large amount of face image data and the slow recognition process speed are the main problem facing face recognition of end devices. This paper proposes a face recognition algorithm based on dual-channel images and adopts a cropped VGG-like model referred as VGG-cut model for predicting. The training set uses the same single-layer images of the same person combined into dual channels as a positive example, and single-layer images of different people are combined into dual channels as a negative example. After model training, the trained face verification model is used as the basis of face recognition, and the final recognition result can be obtained through loop matching and similarity ranking. The experimental results on a RISC-V embedded FPGA platform show that compared with the ResNet model called in Dlib, the VGG-like model and MobileFaceNet trained by Keras, our algorithm is increased by 240×, 88×, and 19× in recognition speed, respectively without significant accuracy reduction.

Face Verification Across Age Progression using Enhanced Convolution Neural Network

This paper proposes a deep learning method for facial verification of aging subjects. Facial aging is a texture and shape variations that affect the human face as time progresses. Accordingly, there is a demand to develop robust methods to verify facial images when they age. In this paper, a deep learning method based on GoogLeNet pre-trained convolution network fused with Histogram Orientation Gradient (HOG) and Local Binary Pattern (LBP) feature descriptors have been applied for feature extraction and classification. The experiments are based on the facial images collected from MORPH and FG-Net benchmarked datasets. Euclidean distance has been used to measure the similarity between pairs of feature vectors with the age gap. Experiments results show an improvement in the validation accuracy conducted on the FG-NET database, which it reached 100%, while with MORPH database the validation accuracy is 99.8%. The proposed method has better performance and higher accuracy than current state-of-the-art methods.

Deep Face Rectification for 360° Dual-Fisheye Cameras.

Rectilinear face recognition models suffer from severe performance degradation when applied to fisheye images captured by 360° back-to-back dual fisheye cameras. We propose a novel face rectification method to combat the effect of fisheye image distortion on face recognition. The method consists of a classification network and a restoration network specifically designed to handle the non-linear property of fisheye projection. The classification network classifies an input fisheye image according to its distortion level. The restoration network takes a distorted image as input and restores the rectilinear geometric structure of the face. The performance of the proposed method is tested on an end-to-end face recognition system constructed by integrating the proposed rectification method with a conventional rectilinear face recognition system. The face verification accuracy of the integrated system is 99.18% when tested on images in the synthetic Labeled Faces in the Wild (LFW) dataset and 95.70% for images in a real image dataset, resulting in an average accuracy improvement of 6.57% over the conventional face recognition system. For face identification, the average improvement over the conventional face recognition system is 4.51%.

Cryptographic Framework for Role Control Remedy: A Secure Role Engineering mechanism for Single Authority Organizations

Role Engineering creates a model for enforcing security among organizations and reduces the risk of entities having unauthorized access privileges. In this paper, we realize the role engineering for the organizations where services are associated with the roles. Access to such services depends on the verification and validation of the role and role ownership. In face to face communication, verification and validation of roles and role ownership are possible through physical certificates but in a computer network, it is spacious. In some cases, it relies on the knowledge-based security systems creating a problem of confidentiality. This research proposed an unconventional Cryptographic Role Engineering (CRE) framework to ensure the privacy and role ownership issues of the entities, secure role management for the role assigning organizations and role validation and verification for the service providing organizations. Validation of the framework is performed through the case study/example scenario and feature analysis. Based on validation and achieved features, it is concluded that the proposed framework achieved the feature of a strong connection between easy administration and strong security. As a result, the role assigning organizations can have a secure role management mechanism, the role owner can provide the proof of role ownership independently and service-providing organizations can verify and validate the role without the intervention of role assigning organizations.

Comparative Analysis of Supervised and Unsupervised Approaches Applied to Large-Scale “In The Wild” Face Verification

Deep learning-based feature extraction methods and transfer learning have become common approaches in the field of pattern recognition. Deep convolutional neural networks trained using tripled-based loss functions allow for the generation of face embeddings, which can be directly applied to face verification and clustering. Knowledge about the ground truth of face identities might improve the effectiveness of the final classification algorithm; however, it is also possible to use ground truth clusters previously discovered using an unsupervised approach. The aim of this paper is to evaluate the potential improvement of classification results of state-of-the-art supervised classification methods trained with and without ground truth knowledge. In this study, we use two sufficiently large data sets containing more than 200,000 “taken in the wild” images, each with various resolutions, visual quality, and face poses which, in our opinion, guarantee the statistical significance of the results. We examine several clustering and supervised pattern recognition algorithms and find that knowledge about the ground truth has a very small influence on the Fowlkes–Mallows score (FMS) of the classification algorithm. In the case of the classification algorithm that obtained the highest accuracy in our experiment, the FMS improved by only 5.3% (from 0.749 to 0.791) in the first data set and by 6.6% (from 0.652 to 0.718) in the second data set. Our results show that, beside highly secure systems in which face verification is a key component, face identities discovered by unsupervised approaches can be safely used for training supervised classifiers. We also found that the Silhouette Coefficient (SC) of unsupervised clustering is positively correlated with the Adjusted Rand Index, V-measure score, and Fowlkes–Mallows score and, so, we can use the SC as an indicator of clustering performance when the ground truth of face identities is not known. All of these conclusions are important findings for large-scale face verification problems. The reason for this is the fact that skipping the verification of people’s identities before supervised training saves a lot of time and resources.

Discriminative metric learning for face verification using enhanced Siamese neural network

Although face verification algorithms have made great success under controlled conditions in recent years, there’s plenty of room at its performance under uncontrolled real-world due to lack of discriminative feature representation ability. From the perspective of metric learning, we proposed a context-aware based Siamese neural network (CASNN) to learn a simple yet powerful network for face verification task to enhance its discriminative feature representation ability. Firstly, a context-aware module is used to automatically focus on the key area of the input facial images without irrelevant background area. Then we design a Siamese network equipped with center-classification loss to compress intra-class features and enlarge between-class ones for discriminative metric learning. Finally, we propose a quantitative indicator named “D-score” to show the discriminative representation ability of the learnt features from different methods. The extensive experiments are conducted on LFW dataset, YouTube Face dataset (YTF) and real-world dataset. The results confirm that CASNN outperforms some state-of-the-art deep learning-based face verification methods.

Age and Gender Classification using Multiple Convolutional Neural Network

Since the advent of social media, there has been an increased interest in automatic age and gender classification through facial images. So, the process of age and gender classification is a crucial stage for many applications such as face verification, aging analysis, ad targeting and targeting of interest groups. Yet most age and gender classification systems still have some problems in real-world applications. This work involves an approach to age and gender classification using multiple convolutional neural networks (CNN). The proposed method has 5 phases as follows: face detection, remove background, face alignment, multiple CNN and voting systems. The multiple CNN model consists of three different CNN in structure and depth; the goal of this difference It is to extract various features for each network. Each network is trained separately on the AGFW dataset, and then we use the Voting system to combine predictions to get the result.

Face Verification Across Age Progression using Enhanced Convolution Neural Network

This paper proposes a deep learning method for facial verification of aging subjects. Facial aging is a texture and shape variations that affect the human face as time progresses. Accordingly, there is a demand to develop robust methods to verify facial images when they age. In this paper, a deep learning method based on GoogLeNet pre-trained convolution network fused with Histogram Orientation Gradient (HOG) and Local Binary Pattern (LBP) feature descriptors have been applied for feature extraction and classification. The experiments are based on the facial images collected from MORPH and FG-Net benchmarked datasets. Euclidean distance has been used to measure the similarity between pairs of feature vectors with the age gap. Experiments results show an improvement in the validation accuracy conducted on the FG-NET database, which it reached 100%, while with MORPH database the validation accuracy is 99.8%. The proposed method has better performance and higher accuracy than current state-of-the-art methods.

A deep learning framework for face verification without alignment

Most of the CNN (convolutional neural networks) methods require alignment, which will affect the efficiency of verification. This paper proposes a deep face verification framework without alignment. First and foremost, the framework consists of two training stages and one testing stage. In the first training stage, the CNN is fully trained on the large face dataset. In the second training stage, embedding triplet is adopted to fine-tune the models. Furthermore, in the testing stage, SIFT (scale invariant feature transform) descriptors are extracted from intermediate pooling results for cascading verification, which effectively improves the accuracy of face verification without alignment. Last but not least, two CNN architectures are designed for different scenarios. The CNN1 (convolutional neural networks 1), with fewer layers and parameters, requires a small amount of memory and computation in training and testing, so it is suitable for real-time system. The CNN2 (convolutional neural networks 2), with more layers and parameters, has excellent face verification. Through the long-term training on WEB-face dataset and experiments on the LFW (labled faces in the wild), YTB (YouTube) datasets, the results show that the proposed method has superior performance compared with some state-of-the-art methods.

Unraveling robustness of deep face anti-spoofing models against pixel attacks

In the last few decades, deep-learning-based face verification and recognition systems have had enormous success in solving complex security problems. However, it has been recently shown that such efficient frameworks are vulnerable to face-spoofing attacks, which has led researchers to build proficient anti-facial-spoofing (or liveness detection) models as an additional security layer. In response, increasingly challenging and tricky attacks have been launched to fool these anti-spoofing mechanisms. In this context, this paper presents the results of an analytical study on transfer-learning-based convolutional neural networks (CNNs) for face liveness detection and differential evolution-based adversarial attacks to evaluate the efficiency of face anti-spoofing classifiers against adversarial attacks. Specifically, experiments were conducted under different use-case scenarios on four face anti-spoofing databases to highlight practical criteria that can be used in the development of countermeasures to address face-spoofing issues.

Analysis of Regression Methods for Face Recognition System

Face verification has recently drawn a lot of attention as one of the best applications of image assessment and perception, particularly over the course of the last significant amount of time. Two factors, namely the wide range of corporate and legal approved applications and the openness of accessible headways after 30 years of research, speak to this example. Despite the fact that existing machine affirmation structures have made some progress, their success is still limited by the requirements of many real-world applications. For instance, the verification of facial images captured in an outside setting with variations in lighting or possibly position isstill a very difficult problem. This paper present a analysis for face recognition method in comparison with PCA and LDA and suggests that LCDRC finds a discriminant subspace by maximizing collaborative between-class reconstruction error and minimizing the within-class reconstruction error simultaneously.

Cross-Age Identity Difference Analysis Model Based on Image Pairs for Age Invariant Face Verification

Face recognition (FR) is a widely studied topic in the field of computer vision research. Although promising results are achieved, FR researches still face challenges of age variations. Most existing FR networks conduct classification based on the feature similarity, which may be misled by large intra class difference under age variations. Instead of calculating feature similarity, in this paper, we derive a novel cross-age face verification framework named Cross-Age Identity Difference Analysis (CIDA) model, which analyzes the identity difference between image pairs under age variations. Specifically, our framework includes two cascading networks. Firstly, an Identity Difference Feature Extractor (IDFE) is proposed to extract the difference information between the input image pair, where the identity discriminant features are effectively extracted, while other interference factors such as age, illumination, posture are suppressed. Secondly, the Direct Cross-age Verification Network (DCVN) is proposed to directly decide whether the input image pair is from the same individual. We derive a novel loss function, where the classification loss with larger age difference is assigned larger weights, which urges the classifier to pay attention to the classification process of the samples with large age gap. Besides, the loss of DCVN are integrated with the loss function of IDFE as a feedback of the final classification performance, improving the discriminant power of IDFE. Through synchronous training of the two networks, we can finally achieve end-to-end network architecture. Compared with the existing cross-age face recognition (CAFR) methods, we do not need to consider feature similarity comparison, which provides a new insight for cross-age face recognition task. Extensive experiments have been performed on the benchmark CAFR datasets which verify the effectiveness of our model.

Low Visual Distortion and Robust Morphing Attacks Based on Partial Face Image Manipulation

Face verification is a popular way for verifying identities in access control systems. In this work, a partial face manipulation-based morphing attack (MA) is proposed to compromise the uniqueness of face templates. Different from existing research, this work changes MA from a holistic face level to component level, and only the most effective facial components (eyes and nose) are used. Therefore, a manipulated face is more similar to a bona fide one in terms of visual quality, texture, and noise characteristics. To validate the effectiveness of the proposed attack, a novel metric called actual mated morph presentation match rate (AMPMR) is proposed to evaluate MA performance under real-world conditions. With a collected dataset containing different attack types, image qualities, and manipulation parameters, the results indicate the proposed attack has better anti-detectability compared with the existing complete, splicing, and combined MAs. Moreover, it has low visual distortion and can reach a better tradeoff among facial biometrics verification, anti-detectability, and visual differences.

Improving face verification using facial marks and deep CNN: IARPA Janus benchmark-A

Face verification performance by human brain has been shown to be much better than most of the state-of-the-art approaches in computer vision. Performance improvement of automated face recognition (FR) systems that may equal or surpass the human intellect is the key goal of this research. We present our face verification system using facial mark (FM) combined with deep convolutional neural network (DCNN) approach to improve the overall FR accuracy. We propose to use FM (e.g., scars, moles and freckles) for face matching in the wild where the FM detection is performed on mean faces as well as affine aligned normalized facial images. The FR experiments are carried out on IARPA Janus Benchmark-A (IJB-A) dataset which includes real-world unconstrained images from 500 subjects. The IJB-A datasets includes full pose, expression, and illumination variations which are much harder than traditional FERET and Mugshot datasets. We evaluated the average FR performance using a weighted score-level fusion of FM and DCNN based recognition methods. The experimental evaluations on FERET, CFM and Mugshot datasets show higher performances than state-of-the-art FM approaches with 99.23%, 94.64% and 97.86% accuracies in Rank-1 evaluations, respectively. Our FR performance of FM + DCNN (86.46% in TAR, 91.23% in Rank-1, 96.57% in Rank-5, 98.65% in Rank-10) is shown to be higher than state-of-the-art (83.80% in TAR@1%FAR, 90.30% in Rank-1, 96.5% in Rank-5, and 97.7% in Rank-10). Experimental results after fusion of FM + DCNN on IJB-A dataset show 2.66% FR performance improvement from the DCNN only recognition in terms of TAR@1%FAR.

CtrlFaceNet: Framework for geometric-driven face image synthesis

Abstract In this work, we introduce a novel framework based on Generative Adversarial Networks to control the pose, expression and facial features of a given face image using another face image. It can then be used for data augmentation, pose invariant face identification, face verification, and lightweight image editing. Generating new realistic face images with controllable poses, facial features, and expressions is a challenging generative learning problem due to skin tone variations, the identity preservation problem, necessity to deal with unseen large poses, and the absence of ground truth images in the training process. We make the following contributions. First, we present a network, CtrlFaceNet that can control a source face image while preserving the identity and skin tone. Second, we introduce a method for training the framework in fully self-supervised mode using a large-scale dataset of unconstrained face images. Third, we show that the style loss function can be used to preserve the skin tone of the source image. The experimental results show that our approach outperforms all other baselines. Furthermore, to the best of our knowledge, we are the first to train such a model using large-scale dataset of unconstrained face images.

Hybrid Siamese Network for Unconstrained Face Verification and Clustering under Limited Resources

In this paper, we propose an unconstrained face verification approach that is dependent on Hybrid Siamese architecture under limited resources. The general face verification trend suggests that larger training datasets and/or complex architectures lead to higher accuracy. The proposed approach tends to achieve high accuracy while using a small dataset and a simple architecture by directly learn face’s similarity/dissimilarity from raw face pixels, which is critical for various applications. The proposed architecture has two branches; the first part of these branches is trained independently, while the other parts shared their parameters. A multi-batch algorithm is utilized for training. The training process takes a few hours on a single GPU. The proposed approach achieves near-human accuracy (98.9%) on the Labeled Faces in the Wild (LFW) benchmark, which is competitive with other techniques that are presented in the literature. It reaches 99.1% on the Arabian faces dataset. Moreover, features learned by the proposed architecture are used in building a face clustering system that is based on an updated version of the Density-Based Spatial Clustering of Applications with Noise (DBSCAN). To handle the cluster quality challenge, a novel post-clustering optimization procedure is proposed. It outperforms popular clustering approaches, like K-Means and spectral by 0.098 and up to 0.344 according to F1-measure.

Two‐tiered face verification with low‐memory footprint for mobile devices

Mobile devices have their popularity and affordability greatly increased in recent years. As a consequence of their ubiquity, these devices now carry all sorts of personal data that should be accessed only by their owner. Even though knowledge-based procedures are still the main methods to secure the owner's identity, recently biometric traits have been employed for more secure and effortless authentication. In this work, the authors propose a facial verification method optimised to the mobile environment. It consists of a two-tiered procedure that combines hand-crafted features and a convolutional neural network (CNN) to verify if the person depicted in a photograph corresponds to the device owner. To train a CNN for the verification task, the authors propose a hybrid-image input, which allows the network to process encoded information of a pair of face images. The proposed experiments show that the solution outperforms state of the art face verification methods, providing a 4× speedup when processing an image in recent smartphone models. Additionally, the authors show that the two-tiered procedure can be coupled with existing face verification CNNs improving their accuracy and efficiency. They also present a new data set of selfie pictures – RECOD Selfie data set – that hopefully will support future research in this scenario.

A weighted exponential discriminant analysis through side-information for face and kinship verification using statistical binarized image features

Side-information based exponential discriminant analysis (SIEDA) is more efficient than side-information based linear discriminant analysis (SILDA) in computing the discriminant vectors because it maximizes the Fisher criterion function. In this paper, we develop a novel criterion, named side-information based weighted exponential discriminant analysis (SIWEDA), that is based on the classical SIEDA method. We reformulate and generalize the classical Fisher criterion function in order to maximize it, with the property to pull as close as possible the intra-class samples (within-class samples), and push and repulse away as far as possible the inter-class samples (between-class samples). Thus, SIWEDA selects the eigenvalues of high significance and eliminate those with less discriminative information. To reduce the feature vector dimensionality and lighten the class intra-variability, we use SIWEDA and within class covariance normalization (WCCN) using the proposed statistical binarized image features (StatBIF). Moreover, we use score fusion strategy to extract the complementarity of different weighting scales of our StatBIF descriptor. We conducted experiments to evaluate the performance of the proposed method under unconstrained environment, using five datasets namely LFW, YTF, Cornell KinFace, UB KinFace and TSKinFace datasets, in the context of matching faces and kinship verification in the wild conditions. The experiments showed that the proposed approach outperforms the current state of the art. Very interestingly, our approach showed superior performance compared to methods based on deep metric learning.

Secure Connected Transactions using Face Verification

In this day and age, cash can be required whenever or anyplace, for example, shopping, voyaging or wellbeing crises and so on. That additionally expands the danger of getting robed. Bank is a most secure spot to keep cash. In any case, consider the possibility that somebody will take your card and by one way or another he/she will know your secret key, it will give him/her full access to your cash. According to the present situation the online exchange is secure with one time secret word (OTP). In age of OTP there are numerous variables that can make OTP special each time it is produced. Right now execute client Identification utilizing Face Recognition to confirm the client. If there should be an occurrence of crisis circumstance the login should be possible utilizing OTP and furthermore the individual picture is caught and Mail to the Account Holder. Accordingly our framework is improve in Security contrasting with the current System.