Face Authentication Research Articles

Inverting Binarizations of Facial Templates Produced by Deep Learning (and Its Implications)

We focus on attacks against a biometric authentication system aimed at reconstructing a biometric sample of the subject from the protected template. Such systems include three blocks: feature extraction, binarization, and protection. We propose a new white-box reversing attack on the binarization block that approximates a biometric template given the binary string obtained by the binarization block. The experiments show that the proposed attack reconstructs very accurate approximations that pass the verification threshold when compared to templates produced from the same and different samples of the subject. We then integrate this attack with known attacks on the other two blocks, namely, a variant of a guessing attack to extract the binary string and biometric inversion attack to reconstruct a sample from its template. We instantiate this end-to-end attack on a face authentication system using fuzzy commitments for protection. Facial images reconstructed by the end-to-end attack greatly resemble the original ones. In the simplest attack scenario, more than 83% of these reconstructed templates succeed in unlocking an account (when the system is configured to 0.1% FMR). Even in the “hardest” settings (in which we take a reconstructed image from one system and use it in a different system, with a different feature extraction process) the reconstructed image offers 170 to 210 times higher success rates than the system’s FMR.

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.

Design and Implementation of a Face Authentication System

Design and Implementation of a Face Authentication System

ENHANCING home security through visual CRYPTOGRAPHY

Home security systems in the recent times have gained greater importance due to increasing threat in the society. Biometrics deals with automated approaches of recognizing a user or verifying the user identity based on behavioral or physiological features. Visual cryptography is a scheme of secret sharing where a secret image is encrypted into shares which disclose no data independently about the original secret image. As the template of biometric are stored in centralized database due to the threats of security the template of biometric may be changed by attacker. If the template of biometric is changed then the authorized user will not be permitted to access the resource. To manage this problem the schemes of visual cryptography can be used to secure the face recognition. Visual cryptography offers huge ways for supporting such needs of security as well as additional authentication layer. To manage this problem the visual cryptography schemes can be used to secure digital biometric information privacy. In this approach the face or private image is dithered in two varied host images that is sheets and are stored in separate servers of data so as to assure that the original image can get extracted only by accessing both sheets together at a time and a single sheet will not be capable to show any data of private image. The main aim of the study is to propose an algorithm which is a combination of CVC and Siamese network. This research implements visual cryptography for face images in a biometric application. The Siamese network is essential to solve one shot learning by representation of learning feature that are compared to verification tasks. In this research face authentication helps in accomplishing robustness by locating face image from an n input image. This research explores the availability of using visual cryptography for securing the privacy to biometric data. The results of the proposed approach provide an accuracy of 93% which is found to be superior when compared with that of the approaches that are already in practice.

Detecting face presentation attacks in mobile devices with a patch-based CNN and a sensor-aware loss function.

With the widespread use of biometric authentication comes the exploitation of presentation attacks, possibly undermining the effectiveness of these technologies in real-world setups. One example takes place when an impostor, aiming at unlocking someone else's smartphone, deceives the built-in face recognition system by presenting a printed image of the user. In this work, we study the problem of automatically detecting presentation attacks against face authentication methods, considering the use-case of fast device unlocking and hardware constraints of mobile devices. To enrich the understanding of how a purely software-based method can be used to tackle the problem, we present a solely data-driven approach trained with multi-resolution patches and a multi-objective loss function crafted specifically to the problem. We provide a careful analysis that considers several user-disjoint and cross-factor protocols, highlighting some of the problems with current datasets and approaches. Such analysis, besides demonstrating the competitive results yielded by the proposed method, provides a better conceptual understanding of the problem. To further enhance efficacy and discriminability, we propose a method that leverages the available gallery of user data in the device and adapts the method decision-making process to the user's and the device's own characteristics. Finally, we introduce a new presentation-attack dataset tailored to the mobile-device setup, with real-world variations in lighting, including outdoors and low-light sessions, in contrast to existing public datasets.

Analysis of RGB-D camera technologies for supporting different facial usage scenarios

Recently a wide variety of applications has been developed integrating 3D functionalities. Advantages given by the possibility of relying on depth information allows the developers to design new algorithms and to improve the existing ones. In particular, for what concerns face morphology, 3D has led to the possibility to obtain face depth maps highly close to reality and consequently an improvement of the starting point for further analysis such as Face Detection, Face Authentication, Face Identification and Face Expression Recognition. The development of the aforementioned applications would have been impossible without the progress of sensor technologies for obtaining 3D information. Several solutions have been adopted over time. In this paper, emphasis is put on passive stereoscopy, structured light, time-of-flight (ToF) and active stereoscopy, namely the most used technologies for the cameras design and fulfilment according to the literature. The aim of this article is to investigate facial applications and to examine 3D camera technologies to suggest some guidelines for addressing the correct choice of a 3D sensor according to the application that has to be developed.

Learning Generalizable and Identity-Discriminative Representations for Face Anti-Spoofing

Face anti-spoofing aims to detect presentation attack to face recognition--based authentication systems. It has drawn growing attention due to the high security demand. The widely adopted CNN-based methods usually well recognize the spoofing faces when training and testing spoofing samples display similar patterns, but their performance would drop drastically on testing spoofing faces of novel patterns or unseen scenes, leading to poor generalization performance. Furthermore, almost all current methods treat face anti-spoofing as a prior step to face recognition, which prolongs the response time and makes face authentication inefficient. In this article, we try to boost the generalizability and applicability of face anti-spoofing methods by designing a new generalizable face authentication CNN (GFA-CNN) model with three novelties. First, GFA-CNN introduces a simple yet effective total pairwise confusion loss for CNN training that properly balances contributions of all spoofing patterns for recognizing the spoofing faces. Second, it incorporate a fast domain adaptation component to alleviate negative effects brought by domain variation. Third, it deploys filter diversification learning to make the learned representations more adaptable to new scenes. In addition, the proposed GFA-CNN works in a multi-task manner—it performs face anti-spoofing and face recognition simultaneously. Experimental results on five popular face anti-spoofing and face recognition benchmarks show that GFA-CNN outperforms previous face anti-spoofing methods on cross-test protocols significantly and also well preserves the identity information of input face images.

A Novel Approach for Improving Dynamic Biometric Authentication and Verification of Human Using Eye Blinking Movement

Biometric authentication is used in Smart devices to lock and unlock devices instead of a PIN. However, using devices in uncontrolled environments is highly susceptible to spoofing by replay attacks. As an example of biometrics is a static face authentication, attackers use face images of spoofed persons to unlock devices, which leads to insecure operations and lack of confidential information without the owner’s knowledge. We have proposed a novel dynamic biometric authentication system (DBAS) based on capturing person eye-blinking movements of a person in addition to face authentication instead of using static face authentication. Proposed DBAS take a sequence of using images as input by asking the user to follow an eye-blink sequence. Then proposed DBAS unlock the mobile devices based on the correct eye-blink sequence made by the person. The test results show that our method is effective and robust for the screen unlocks on smart devices with 98.4% accuracy.

Adversarial examples for replay attacks against CNN-based face recognition with anti-spoofing capability

In the race of arms between attackers, trying to build more and more realistic face replay attacks, and defenders, deploying spoof detection modules with ever-increasing capabilities, CNN-based methods have shown outstanding detection performance thus raising the bar for the construction of realistic replay attacks against face-based authentication systems. Rather than trying to rebroadcast even more realistic faces, we show that attackers can successfully fool a face authentication system equipped with a deep learning spoof detection module, by exploiting the vulnerabilities of CNNs to adversarial perturbations. We first show that mounting such an attack is not a trivial task due to the unique features of spoofing detection modules. Then, we propose a method to craft adversarial images that can be successfully exploited to build an effective replay attack. Experiments conducted on the REPLAY-MOBILE database demonstrate that our attacked images achieve good performance against a face recognition system equipped with CNN-based anti-spoofing, in that they are able to pass the face detection, spoof detection and face recognition modules of the authentication chain.

Face Recognition Algorithm Based On Convolutional Neural Network

In response to the needs of modern enterprises for intelligent security of face authentication, a face recognition algorithm based on deep convolutional neural network is introduced in this paper. In order to improve the accuracy of face recognition, the design of 11 convolutional layers and 4 pooling layers. The network uses the standard face data set CASIA-WebFace for training, and the face recognition accuracy on the LTW database can reach more than 97.8%. Based on this deep learning network, a face recognition management system was designed. The system realized 1: 1 face authentication and 1: N face recognition. After applying the convolutional neural network algorithm, the face search time is significantly reduced, indicating that the algorithm has higher efficiency and practicability.

More time for science: Using Testable to create and share behavioral experiments faster, recruit better participants, and engage students in hands-on research.

A major pain for researchers in all fields is that they have less and less time for actual science activities: reading, thinking, coming up with new theories and hypotheses, testing, analyzing data, writing. In psychology, three of the most time-consuming nonactual science activities are: learning how to program an experiment, recruiting participants, and preparing teaching materials. Testable (www.testable.org) provides a suite of academic tools to speed things up considerably. The Testable software allows the development of most psychology experiments in minutes, using a natural language form and a spreadsheet. Furthermore, any experiment can be easily converted into a social experiment in Testable Arena, with multiple participants interacting and viewing each other's responses. Experiments can then be published to Testable Library, a public repository for demonstration and sharing purposes. Participants can be recruited from Testable Minds, the subject pool with the most advanced participants verification system. Testable Minds employs multiple checks (such as face authentication) to ensure participants have accurate demographics (age, sex, location), are human, unique, and reliable. Finally, the Testable Class module can be used to teach psychology through experiments. It features over 50 ready-made classic psychology experiments, fully customizable, which instructors can add to their classes, together with their own experiments. These experiments can then be made available to students to do, import, modify, and use to collect data as part of their class. These Testable tools, backed up by a strong team of academic advisors and thousands of users, can save psychology researchers and other behavioral scientists valuable time for science.

A Hybrid Global Local Adaptive Particle Swarm Optimization-Based Support Vector Machine Model for Human Facial Authentication

A new facial authentication model called global local adaptive particle swarm optimization-based support vector machine, was proposed in this paper. The proposed model aimed to solve the problem of finding the preeminent parameters of support vector machine in order to come out with a powerful human facial authentication technique. The conventional particle swarm optimization algorithm was utilized with support vector machine to explore the preeminent parameters of support vector machine. However, the particle swarm optimization support vector machine model has some limitations in selecting the velocity coefficient and inertia weight. One of the best approaches, which is used to solve the velocity coefficient problem, is adaptive acceleration particle swarm optimization. Also, the global-local best inertia weight is used efficiently for selecting the inertia weight. Therefore, the global local adaptive particle swarm optimization-based support vector machine model was proposed based on combining adaptive acceleration particle swarm optimization, global-local best inertia weight, and support vector machine. The proposed model used the principal component analysis approach for feature extraction, as well as global local adaptive particle swarm optimization for finding the preeminent parameters of support vector machine. In the experiments, two datasets (YALEB and CASIAV5) were used, and the suggested model was compared with particle swarm optimization support vector machine and adaptive acceleration particle swarm optimization support vector machine methods. The comparison was via accuracy, computational time, and optimal parameters of support vector machine. Our model can be used for security applications and apply for human facial authentication.

Practical Privacy-Preserving Face Authentication for Smartphones Secure Against Malicious Clients

We propose a privacy-preserving face authentication system for smartphones that guarantees security against malicious clients. Using the proposed system, a face feature vector is stored on a remote server in encrypted form. To guarantee security against an honest-but-curious server who may try to learn the private feature vector, we perform a Euclidean distance-based matching score computation on encrypted feature vectors using homomorphic encryption. To provide security against malicious clients, we adopt a blinding technique. We implement the proposed system on a mobile client and a desktop server. Through an experiment with real-world participants, we demonstrate that secure face verification can be completed in real time (within 1.3 s) even when a smartphone is involved, with an Equal Error Rate (EER) of 3.04%. In further experiments with two public face datasets, CFP and ORL, face verification is completed in approximately 1 s with EER of 1.17% and 0.37%, respectively. Our system is two orders of magnitude faster than previous privacy-preserving face verification method with the same security assumptions and functionalities. To achieve this secure real-time computation, we improve the Catalano-Fiore transformation which converts a linear homomorphic encryption scheme into a quadratic scheme, and parallelize the decryption procedure of our system.

Object-specific face authentication system for liveness detection using combined feature descriptors with fuzzy-based SVM classifier

Object-specific face authentication system for liveness detection using combined feature descriptors with fuzzy-based SVM classifier

Object-specific face authentication system for liveness detection using combined feature descriptors with fuzzy-based SVM classifier

The object face liveness detection for genuine face recognition and user authentication is a difficult task and day to day it becoming an interesting tricky in real time vision and security applications. Since many decades, various authors have proposed new technique and methods and developed but still the system has to improve to recognise the genuine object faces from spoofing objects with increasing in accuracy. However, in account of existing methods were fails in finding of genuine objects from sample objects and individual differences between them. The ordinary classifier cannot simplifies well to various kind of objects in different directions especially in case of blur images. In order to overcome his problem, we proposed an object-specific face authentication system for liveness detection using combined feature descriptors with fuzzy-based SVM classifier, allows to select specific area from whole object, extract features from specific area of object leads reduction in processing time and complexity in feature extraction. Later the system recognise respective faces, finally it checks for live objects with the help of fuzzy logic-based SVM classifier. With these proposed system, makes it practical to train well performed individual object to its certain face with liveness detection and achieved improvement in performance and accuracy.

Message from Facial Studies using Image AI Technology for face authentication and facial caricaturing

Message from Facial Studies using Image AI Technology for face authentication and facial caricaturing

Cancelable Face Template Protection using Transform Features for Cyberworld Security

Cyber world becomes a fundamental and vital component of the physical world with the increase of dependence on internet-connected devices in industry and government organizations. Provision of privacy and security of users during online communication offers unique cybersecurity challenges for industry and government. Intrusion is one of the crucial issues of cybersecurity, which can be overcome by providing the vigorous authentication solutions. Biometrics authentication is used in different cybersecurity systems for user authentication purpose. The cancelable biometric is a solution to rid of privacy problems in traditional biometric systems. This paper purposes a new cancelable face authentication method, which uses Hybrid Gabor PCA (HGPCA) descriptor for cyberworld security. The proposed method uses the wavelet transform for the extraction of the features of the face images by using Gabor filter and Principal Component Analysis (PCA). Later, both types of features have been ensemble by using the simple concatenation scheme. Then scrambling has been applied to the fused features by using the random key generated by the user. So finally, scrambled fused features has been stored in the database which are used for the cancelable biometric authentication as well as recovery. HGPCA achieves “cancelability” and increases the authentication accuracy. The proposed method has been tested on three standard face datasets. Experimental results of the proposed method have been compared with existing methods by using standard quantitative measures that show superiority over existing methods.

3D Face Authentication Software Test Automation

The 3D face authentication has become a hot trend for researchers and developers in the recent years, due to its many advantages over the 2D face recognition feature. The 3D reconstruction of a human face using both near-infrared and depth sensors is a complex process on mobile phones. It commonly involves algorithms like face detection, face landmark detection, facial feature extraction, and depth information analysis. The 3D face authentication feature is critical for the user as per as security and also providing a convenient way to authenticate by the correct user. Therefore, the testing of 3D face authentication algorithms and applications in terms of functionality, performance, and stability is critical. However, the research on 3D face authentication application level validation and testing method is lacking in the field. Most testers are still validating the application manually. In this paper, we propose a robotic-arm-based test automation for testing the 3D face authentication feature on mobile phones. We programmed a 6 degree of freedom (6-DOF) robotic arm to perform 3D face authentication automated tests that were executed manually before. Our test automation also benchmarked the performance of an in-house developed 3D face authentication application and a 3 rd party application which yielded promising latency and accuracy comparison results under different performance-impacting test scenarios.

EchoFace: Acoustic Sensor-Based Media Attack Detection for Face Authentication

Face authentication systems have gained widespread popularity because of their user-friendly usage and increasing recognition accuracy. Unfortunately, the boom in mobile social networks has bought with it media-based facial forgery; a critical threat where an adversary forges or replays the victim’s photograph/video to fool the system. In this article, we propose EchoFace, an effective and robust liveness detection system to enhance face authentication in defending against media-based attacks, which works with today’s smartphones/smartwatches without any hardware modification. EchoFace uses active acoustic sensing to differentiate the uneven stereostructure of the face and the flat forged media. Our proposed scheme effectively extracts the desired reflection profiles from the target. Moreover, we propose effective similarity measurements of reflection profiles to distinguish live users from forged media, which works robustly under various environmental conditions. EchoFace only requires low cost and universally equipped acoustic sensors without human intervention for liveness detection, which can be easily deployed in a variety of application scenarios. We implement EchoFace on commercial smartphones, and experiment results show that EchoFace achieves an average detection accuracy higher than 96% and false alarm rate lower than 4% across various media attacks and different levels of background noise. This shows its great potential to enhance the security of widely deployed face authentication systems in real scenarios.

SS1-KL-1 APPLICATION OF AI TECHNOLOGIES FOR MEDICAL CARE

On the basis of progress of the Machine Learning algorithm mainly on the Deep Learning, improvement of the GPU performance, the large-scale public database such as TCGA is available, big attention recently gathers in the AI technology. While large countries such as the United States or China vigorously promote AI research and development by a national policy, Cabinet Office, Government of Japan, also emphasized the importance of AI technologies in the 5th Science and Technology Basic Plan in 2016. As for the AI development, it is wrestled relatively for a long time; the word “Artificial Intelligence” was firstly used in the Dartmouth workshop in 1956. However, the AI development has not been promoted smoothly until now and repeats the active state period and the period of depression. As the current active state period of AI is called as the third AI boom, the most different point of this boom and the other booms is that AI technologies have already been involved in our social life such as the AI-based face authentication device in this period. Indeed, The US Food and Drug Administration (FDA) has already authorized around 30 AI-based medical instruments, and the Pharmaceuticals and Medical Devices Agency (PMDA) in Japan also authorized the first AI-based medical instrument last year. Therefore, now is the important time that we need to consider deeply for the creation of an affluent society, which enables coexistence of human being and AI. In this lecture, I particularly focus on medical imaging analysis using AI technologies and, would like to lecture on an action to the medical care application of the AI technology based on the experience that promoted medical AI research as the leader of two national projects relevant to medical AI called CREST and PRISM, and RIKEN AIP center.