Face Verification Research Articles

Detecting and identifying Schistosoma infections in snails and aquatic habitats: A systematic review.

BackgroundWe were tasked by the World Health Organization (WHO) to address the following question: What techniques should be used to diagnose Schistosoma infections in snails and in the water in potential transmission sites? Our goal was to review and evaluate the available literature and provide recommendations and insights for the development of WHO’s Guidelines Development Group for schistosomiasis control and elimination.MethodologyWe searched several databases using strings of search terms, searched bibliographies of pertinent papers, and contacted investigators who have made contributions to this field. Our search covered from 1970 to Sept 2020. All papers were considered in a PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework, and retained papers were grouped by technique and subjected to our GRADE (Grading of Recommendations, Assessment, Development and Evaluations) evidence assessment profile determined in consultation with WHO. We also considered issues of sensitivity, specificity, coverage, cost, robustness, support needs, schistosome species discrimination, and relevant detection limits.Principal findingsOur PRISMA process began with the perusal of 949 articles, of which 158 were retained for data extraction and evaluation. We identified 25 different techniques and for each applied a GRADE assessment considering limitations, inconsistency, imprecision, indirectness, and publication bias. We also provide advantages and disadvantages for each category of techniques.ConclusionsOur GRADE analysis returned an assessment of moderate quality of evidence for environmental DNA (eDNA), qPCR and LAMP (Loop-mediated isothermal amplification). No single ideal diagnostic approach has yet been developed, but considerable recent progress has been made. We note a growing trend to use eDNA techniques to permit more efficient and replicable sampling. qPCR-based protocols for follow-up detection offer a versatile, mature, sensitive, and specific platform for diagnosis though centralized facilities will be required to favor standardization. Droplet digital PCR (ddPCR) can play a complementary role if inhibitors are a concern, or more sensitivity or quantification is needed. Snail collection, followed by shedding, is encouraged to provide specimens for sequence verifications of snails or schistosomes. LAMP or other isothermal detection techniques offer the prospect of less expensive and more distributed network of analysis but may face standardization and verification challenges related to actual sequences amplified.Ability to detect schistosome infections in snails or in the water is needed if control and elimination programs hope to succeed. Any diagnostic techniques used need to be regularly verified by the acquisition of DNA sequences to confirm that the detected targets are of the expected species. Further improvements may be necessary to identify the ideal schistosome or snail sequences to target for amplification. More field testing and standardization will be essential for long-term success.

Face perception foundations for pattern recognition algorithms

Pattern recognition system developers have looked in multiple directions over the years and designed a broad spectrum of methodologies for face identification and verification, both in 2D and 3D. These techniques rely on sound methods and experimentations, and currently give high to excellent recognition rates in terms of performance. Nonetheless, it seems that the most performing face recognition system, especially when familiar faces are involved, is still the human being, able to detect known faces in the wild, in presence of occlusions or extreme light contrast, caricatures, sketches, partial views, blurred images. This is one of the manifold reasons why the human visual system at eye and brain level and face perception techniques are currently being studied by neuroscientists and psychologists, with the aim to uncover the processes underneath the human vision.The purpose of this work is to review the current literature about perception foundations and related biologically-inspired methodologies for face recognition.

Face verification algorithm based on Angular Margin triplet

In recent years, the face recognition algorithm based on CNN mainly focuses on the improvement of loss function. This paper improves the softmax loss function from two aspects: one is to normalize the weight and features of the loss function, the other is to introduce in class cosine similarity judgment on the basis of the original loss function. These improvements eventually make the distribution of features within the class closer and the distance between classes as far as possible. The recognition accuracy and generalization ability of the network are enhanced.

Towards a self-sufficient face verification system

The absence of a previous collaborative manual enrolment represents a significant handicap towards designing a face verification system for face re-identification purposes. In this scenario, the system must learn the target identity incrementally, using data from the video stream during the operational authentication phase. So, manual labelling cannot be assumed apart from the first few frames. On the other hand, even the most advanced methods trained on large-scale and unconstrained datasets suffer performance degradation when no adaptation to specific contexts is performed. This work proposes an adaptive face verification system, for the continuous re-identification of target identity, within the framework of incremental unsupervised learning. Our Dynamic Ensemble of SVM is capable of incorporating non-labelled information to improve the performance of any model, even when its initial performance is modest. The proposal uses the self-training approach and is compared against other classification techniques within this same approach. Results show promising behaviour in terms of both knowledge acquisition and impostor robustness.

An Extensive Study on Traditional-to-Recent Transformation on Face Recognition System

The face is the most visible biometric feature considered in manual and automated systems to identify individuals. In recent years, the inventions of low-cost high-resolution mobile cameras and surveillance systems have increased the usage and popularity of facial recognition systems. Today, various mobile apps, ICT systems, electronic devices, and cloud-based systems are using facial verification to gain access and authorization based security. Now, the facial recognition system has become a generalized authentication system instead of a specialized capability of specialized applications. This increasing scope and applications are reflected as the challenges and criticalities that exist in the real environment. The technological transformation was investigated by the researchers in image types, facial capturing, facial rectification, feature generation, and classification methods and techniques of face recognition systems. This paper has provided an extensive study on each stage of standard FRS (Face Recognition System). The methods and measures provided by researchers for improving and optimizing face normalization, segmentation, feature generation, and face recognition are provided in this paper. Each of the methods is also defined along with the performance, accuracy, and robustness against various addressed challenges. The paper has provided a map between the traditional and recent methods to explore the advancement in facial verification systems.

Learning deep discriminative embeddings via joint rescaled features and log-probability centers

Recently softmax based loss functions have surged to advance image classification and face verification. Most efforts boost discrimination of the softmax loss by using novel angular margins in varying ways, but few analyze where the discrimination truly comes from whilst considering the power of relieving the overfitting to enhance the softmax loss. In this paper, we firstly delve into such mainstream of softmax based loss functions in theory, and recognize the importance of easing overfitting to the softmax loss. In terms of such analysis, this paper intends to bring the softmax loss up to the competitive level with current well-behaved loss functions. We do this in two ways: (1) regularizing the softmax to relieve the overfitting by learning the log-probability centers, and (2) rescaling deep embeddings of the softmax with a constant scale to further enhance inter-class separability in Euclidean space. We call the resulting loss function rLogCenter loss for short. Simple and interpretable as our loss is, it guides CNNs to induce performance gains in the experiments of both image classification and face verification.

A Master Key backdoor for universal impersonation attack against DNN-based face verification

We introduce a new attack against face verification systems based on Deep Neural Networks (DNN). The attack relies on the introduction into the network of a hidden backdoor, whose activation at test time induces a verification error allowing the attacker to impersonate any user. The new attack, named Master Key backdoor attack, operates by interfering with the training phase, so to instruct the DNN to always output a positive verification answer when the face of the attacker is presented at its input. With respect to existing attacks, the new backdoor attack offers much more flexibility, since the attacker does not need to know the identity of the victim beforehand. In this way, he can deploy a Universal Impersonation attack in an open-set framework, allowing him to impersonate any enrolled users, even those that were not yet enrolled in the system when the attack was conceived. We present a practical implementation of the attack targeting a Siamese-DNN face verification system, and show its effectiveness when the system is trained on VGGFace2 dataset and tested on LFW and YTF datasets. According to our experiments, the Master Key backdoor attack provides a high attack success rate even when the ratio of poisoned training data is as small as 0.01, thus raising a new alarm regarding the use of DNN-based face verification systems in security-critical applications.

Polyhedral Conic Classifiers for Computer Vision Applications and Open Set Recognition.

This paper introduces a family of quasi-linear discriminants that outperform current large-margin methods in sliding window visual object detection and open set recognition tasks. In these applications, the classification problems are both numerically imbalanced - positive (object class) training and test windows are much rarer than negative (non-class) ones - and geometrically asymmetric - the positive samples typically form compact, visually-coherent groups while negatives are much more diverse, including anything at all that is not a well-centered sample from the target class. For such tasks, there is a need for discriminants whose decision regions focus on tightly circumscribing the positive class, while still taking account of negatives in zones where the two classes overlap. To this end, we propose a family of quasi-linear "polyhedral conic" discriminants whose positive regions are distorted L1 or L2 balls. In addition, we also integrated the proposed classification loss into deep neural networks so that both the features and classifier can be learned simultaneously end-to-end fashion to improve the classification accuracies. The methods have properties and run-time complexities comparable to linear Support Vector Machines (SVMs), and they can be trained from either binary or positive-only samples using constrained quadratic programs related to SVMs. Our experiments show that they significantly outperform linear SVMs, deep neural networks using softmax loss function and existing one-class discriminants on a wide range of object detection, face verification, open set recognition and conventional closed-set classification tasks.

CULTURAL ADAPTATION AND VALIDATION OF THE MORAL SENSITIVITY QUESTIONNAIRE AMONG BRAZILIAN NURSES

ABSTRACT Objective: cultural adaptation and validation of the Moral Sensitivity Questionnaire to assess moral sensitivity among Brazilian nurses. Method: methodological study with 106 nurses from two hospitals located in the south of Brazil. The instrument was culturally adapted according to international guidelines, following six steps: translation, reconciliation of translated versions, back translation, expert panel, pretest, and final review. The Brazilian version was validated in the Brazilian context using factor analysis and Cronbach’s alpha. Results: after the expert panel’s assessment, pretest, and verification of face and content validity, the instrument was considered satisfactory to be applied among Brazilian nurses. Six constructs were identified: Respect for patients’ autonomy, modified autonomy, experiencing moral conflict, having confidence in medical and nursing knowledge, structuring moral meanings, and teamwork. The instrument presented satisfactory internal consistency, with a Cronbach’s alpha equal to 0.62; the Cronbach’s alpha of the six constructs ranged from 0.60 to 0.67. Conclusion: the Brazilian version of the Moral Sensitivity Questionnaire is valid and reliable to be applied among Brazilian nurses and can improve understanding of factors involving decision-making when facing ethical conflicts.

Deep Polynomial Neural Networks.

Deep convolutional neural networks (DCNNs) are currently the method of choice both for generative, as well as for discriminative learning in computer vision and machine learning. The success of DCNNs can be attributed to the careful selection of their building blocks (e.g., residual blocks, rectifiers, sophisticated normalization schemes, to mention but a few). In this paper, we propose Π-Nets, a new class of function approximators based on polynomial expansions. Π-Nets are polynomial neural networks, i.e., the output is a high-order polynomial of the input. The unknown parameters, which are naturally represented by high-order tensors, are estimated through a collective tensor factorization with factors sharing. We introduce three tensor decompositions that significantly reduce the number of parameters and show how they can be efficiently implemented by hierarchical neural networks. We empirically demonstrate that Π-Nets are very expressive and they even produce good results without the use of non-linear activation functions in a large battery of tasks and signals, i.e., images, graphs, and audio. When used in conjunction with activation functions, Π-Nets produce state-of-the-art results in three challenging tasks, i.e., image generation, face verification and 3D mesh representation learning. The source code is available at https://github.com/grigorisg9gr/polynomial_nets.

Weakly Supervised Compositional Metric Learning for Face Verification

The aim of metric learning is to learn a mapping relationship, which reduces the intraclass distance and increases the interclass distance. As there are a large number of parameters that need to be optimized in traditional metric learning algorithms, they may suffer from high computational complexity and overfitting problems in the case of insufficient training data. To alleviate this, we propose a weakly supervised compositional metric learning (WSCML) method, which utilizes a set of predetermined local discriminant metrics to learn the optimal weight combination of the component metrics. Under the large margin framework, our WSCML can effectively improve the verification accuracy by constraining the Mahalanobis distance of positive sample pairs to be less than a small threshold and that of negative sample pairs to be greater than a large threshold. In addition, we employ three regularization terms to optimize the proposed WSCML, respectively, to control the sparsity of the solution while maintaining its feasibility. Experimental results on KinFaceW-I, fine-grained face verification (FGFV), and Labled Faces in the Wild (LFW) datasets show the effectiveness of the proposed method.

The Ethics of Facial Recognition Technology

This is a comprehensive presentation of leading ethical issues in debates about facial recognition technology. After defining basic terms (facial detection, facial characterization, facial verification, and facial identification), the following issues are discussed: standards, measures, and disproportionately distributed harms; erosions of trust; ethical harms associated with perfect facial surveillance; alienation, dehumanization, and loss of control; and the slippery slope debate.

Audio-Visual Deep Neural Network for Robust Person Verification

Voice and face are two most popular biometrics for person verification, usually used in speaker verification and face verification tasks. It has already been observed that simply combining the information from these two modalities can lead to a more powerful and robust person verification system. In this article, to fully explore the multi-modal learning strategies for person verification, we proposed three types of audio-visual deep neural network (AVN), including feature level AVN (AVN-F), embedding level AVN (AVN-E), and embedding level combination with joint learning AVN (AVN-J). To further enhance the system robustness in real noisy conditions where not both modalities can be accessed with high-quality, we proposed several data augmentation strategies for each proposed AVN: A feature-level multi-modal data augmentation is proposed for AVN-F and an embedding-level data augmentation with novel noise distribution matching is designed for AVN-E. For AVN-J, both the feature and embedding level multi-modal data augmentation methods can be applied. All the proposed models are trained on the VoxCeleb2 dev dataset and evaluated on the standard VoxCeleb1 dataset, and the best system achieves 0.558, 0.441% and 0.793% EER on the three official trial lists of VoxCeleb1, which is to our knowledge the best published single system results on this corpus for person verification. To validate the robustness of the proposed approaches, a noisy evaluation set based on the VoxCeleb1 is constructed, and experimental results show that the proposed system can significantly boost the system robustness and still show promising performance under this noisy scenario.

MTCNN and FACENET Based Access Control System for Face Detection and Recognition

Face detection and recognition is one of the research hotspots in the field of computer vision, which is widely used in video surveillance and identity matching. The traditional algorithms of face detection include AdaBoost, Haar-like, DPM, etc. These algorithms use face geometric features and template matching for face detection, which is difficult to consider both detection speed and accuracy. In order to solve this problem, this paper proposes an improved FaceNet network. The multi-task cascaded convolutional neural networks (MTCNN) is used to achieve rapid face detection and face alignment, and then the FaceNet with improved loss function is used to realize face verification and recognition with high accuracy. We compare the face detection and recognition performance of our network framework with the traditional algorithms and some deep learning algorithms. The experimental results verify that the improved FaceNet can meet the requirements of real-time recognition, and the recognition accuracy reaches 99.85%, which meets the actual demand. It can be effectively applied to face detection and recognition in the Access Control System.

Automated Artifact Retouching in Morphed Images With Attention Maps

Morphing attack is an important security threat for automatic face recognition systems. High-quality morphed images, i.e. images without significant visual artifacts such as ghosts, noise, and blurring, exhibit higher chances of success, being able to fool both human examiners and commercial face verification algorithms. Therefore, the availability of large sets of high-quality morphs is fundamental for training and testing robust morphing attack detection algorithms. However, producing a high-quality morphed image is an expensive and time-consuming task since manual post-processing is generally required to remove the typical artifacts generated by landmark-based morphing techniques. This work describes an approach based on the Conditional Generative Adversarial Network paradigm for automated morphing artifact retouching and the use of Attention Maps to guide the generation process and limit the retouch to specific areas. In order to work with high-resolution images, the framework is applied on different facial crops, which, once processed and retouched, are accurately blended to reconstruct the whole morphed face. Specifically, we focus on four different squared face regions, i.e. the right and left eyes, the nose, and the mouth, that are frequently affected by artifacts. Several qualitative and quantitative experimental evaluations have been conducted to confirm the effectiveness of the proposal in terms of, among the others, pixel-wise metrics, identity preservation, and human observer analysis. Results confirm the feasibility and the accuracy of the proposed framework.

PFA-GAN: Pose Face Augmentation Based on Generative Adversarial Network

In this work, we propose a novel framework based on Generative Adversarial Networks for pose face augmentation (PFA-GAN). It enables a controlled pose synthesis of a new face image from a source face given a driving one while preserving the identity of the source face. We introduce a method for training the framework in a fully self-supervised mode using a large-scale dataset of unconstrained face images. Besides, some augmentation strategies are presented to expand the training set. The face verification experimental results demonstrate the effectiveness of the presented augmentation strategies as all augmented datasets outperform the baseline.

SqueezeFace: Integrative Face Recognition Methods with LiDAR Sensors

In this paper, we propose a robust and reliable face recognition model that incorporates depth information such as data from point clouds and depth maps into RGB image data to avoid false facial verification caused by face spoofing attacks while increasing the model’s performance. The proposed model is driven by the spatially adaptive convolution (SAC) block of SqueezeSegv3; this is the attention block that enables the model to weight features according to their importance of spatial location. We also utilize large‐margin loss instead of softmax loss as a supervision signal for the proposed method, to enforce high discriminatory power. In the experiment, the proposed model, which incorporates depth information, had 99.88% accuracy and an F1 score of 93.45%, outperforming the baseline models, which used RGB data alone.

Face Detection and Recognition Techniques through the Cloud Network: An Exploratory Study

Face recognition is one of the fundamental functions performed by biometrics, and it is becoming increasingly influential as new technologies like the internet and digital cameras require improved security critical features. Other applications also make use of face recognition. Face recognition software can work with static photos or visual sequences to accomplish tasks. In addition, it can handle either one of the following tasks: face identification (also known as face recognition) or face verification (also known as face authentication). People can quickly and reliably recognize known faces and identities, even when presented with challenging viewing conditions such as changing illuminations, occlusion, scale, or rotation. This ability is a hallmark of the human species. Motivated by its significance in human-to-human communication and leading to various applications, ranging from biometrics to human-computer interaction, the face recognition challenge is an essential issue in the field of computer vision as well as other related areas. Finally, this article provides a summary of the most recent and cutting-edge strategies that have been developed to deal with challenging tasks like the one being discussed.

Pairwise Ranking Distillation for Deep Face Recognition

This work addresses the problem of knowledge distillation for deep face recognition task. Knowledge distillation technique is known to be an effective way of model compression, which implies transferring of the knowledge from high-capacity teacher to a lightweight student. The knowledge and the way how it is distilled can be defined in different ways depending on the problem where the technique is applied. Considering the fact that face recognition is a typical metric learning task, we propose to perform knowledge distillation on a score-level. Specifically, for any pair of matching scores computed by teacher, our method forces student to have the same order for the corresponding matching scores. We evaluate proposed pairwise ranking distillation (PWR) approach using several face recognition benchmarks for both face verification and face identification scenarios. Experimental results show that PWR not only can improve over the baseline method by a large margin, but also outperforms other score-level distillation approaches.

RETRACTED: Research on 3D to 2D atlas of tujiayang opera mask characters based on fuzzy image reconstruction

Abstract Character library routine is executed for the frame, and the mask is a space for establishing the connection—a graphical summary of each cover everything thing as a clear letter. By showing the arch together all the masks, the viewer is connected with the universe of letters. Also intend to build from the inside and outside to see another way to display the complex links between our internal and external world. By showing photographs or record player in the most offensive 2D recording zero return, these significant bit tests are rooted in the conflict before the flatness sensor material network. Ability Tsujiayang Opera Mask Character Processing (TOMCP) of a three-dimensional human face cover Wi-Fi singular 2D surface in this article, the method will examine with a positive response. Besides, through Kinect acquired, regardless of the tone and depth of information, the counter estimates the counter to investigate the local binary mode. For this reason, 3D, effortlessly capturing the depth camera, put forward a Masked Attack Database (3DMAD), a three-dimensional sculpture of information infrastructure. A wide range of effective and feasible to represent 2D face cover face verification frame real danger, 3DMAD display check, for example in the manufacture, processing, quality awareness and lack of control of parts of the great weapon to kill the industry cycle propulsion method of computing vision, image processing seems to be the basis of a fluffy, has become part of research in the field of to system. Item, especially at the edges of the captured image, is detected by a mechanical process using a computer framework. Tsujiayang Opera Mask Character Processing (TOMCP) extends the possible outcomes, solves problems, and offers many answers beyond traditional policy limitations.