FACE VERIFICATION SYSTEM IN MOBILE DEVICES BY USING COGNITIVE SERVICES

This paper presents an automatic video based face verification and recognition system by Support Vector Machines (SVMs). Faces as training samples are automatically extracted from input video sequences in real-time by LUT-based Adaboost and are normalized both in geometry and in gray level distribution after facial landmark localization via Simple Direct Appearance Model (SDAM). Two different strategies for multi-class face verification and recognition problems with SVMs, one-vs-all and one-vs-another, are discussed and compared in details. Experiment results over 100 clients are reported to demonstrate the effectiveness of SVM on video sequences.

For the past years, studies on face recognition were focused on the effects and influence of illumination, pose, and angle to the performance, which, in fairness, has been proven by different research works as the dominant factor of adding complexity to face recognition across age progression observed through photographs, specifically headshots (Ling, Soatto, Ramanathan, & Jacobs, 2007; Ramanathan & Chellappa, 2006; Ramanathan & Chellappa, 2005). However, studies on face verification and recognition with time lapses between pairs of photographs and the interaction on the sexual orientation of the identifier was somehow defocused on the factor of effects of age progression or the time interval between pairs of facial images on face recognition. Thus, the researchers aimed to resolve the gap on the prevailing literatures on face recognition by finding the effects of age interval of the stimuli and sexual orientation of the participants on face recognition using Filipino college students as sample. Results showed that the greater the age gaps between the pair of images, the lesser the chance of identifying it. In addition, it is also not important to consider whether identifier is male or female in terms of recognizing picture because based from the result of the experiment, sexual orientation does not have an effect on the performance of the participants in face recognition. The study was guided by the theory of Biederman (1987) and Recognition By Components (RBC) to analyze the findings.

Advancements in the field of biometrics have led to the development of face identification and verification systems. The problem of face verification faces many challenges; however, the challenge of progressing age for verification of human faces remains to be the most challenging of all. The work in the field of face verification across age progression is limited and the field requires more attention and research. This work proposes a technique for face verification which can handle large age gaps. The proposed technique extracts Center Symmetric Local Binary Patterns (CSLBP) to represent the features of the face image. CSLBP has been widely used as a texture descriptor in face recognition. The performance of CSLBP features when combined with weighted K-Nearest Neighbor classifier is best when compared with Support Vector Machine and Tree based classifiers. This work studies the performance of the proposed method on increasing age gaps. Usually, the performance of any face verification system deteriorates or stagnates as the age gap increases; however, the performance of the proposed method improves as the age gap increases. The experiments are performed on MORPH database.

In this paper, we present an extensive evaluation of face recognition and verification approaches performed by the European COST Action MULTI-modal Imaging of FOREnsic SciEnce Evidence (MULTI-FORESEE). The aim of the study is to evaluate various face recognition and verification methods, ranging from methods based on facial landmarks to state-of-the-art off-the-shelf pre-trained Convolutional Neural Networks (CNN), as well as CNN models directly trained for the task at hand. To fulfill this objective, we carefully designed and implemented a realistic data acquisition process, that corresponds to a typical face verification setup, and collected a challenging dataset to evaluate the real world performance of the aforementioned methods. Apart from verifying the effectiveness of deep learning approaches in a specific scenario, several important limitations are identified and discussed through the paper, providing valuable insight for future research directions in the field.

Secure Learning using Face Recognition has gained significant attention due to its potential solution in curbing and elimination of electronic examination impersonation through enrollment, authentication and authorization. Despite the advancements, existing system often focuses on individual entities, void of models and special dataset to enhance security measure in comprehensively curbing electronic examination impersonation. This study aims to address this gap by performing a model using face recognition: Labeled Face in the Wild (LFW), Custom Dataset, algorithms using TensorFlowJs and Face Application Programming Interface (API) model. The research evaluates this model on a unified facial recognition dataset, assessing their performance based on face verification and recognition. The LFW dataset a popular benchmark dataset for face verification and recognition focuses on detecting and alignment of captured images, where TensorFlowJs extract face embedding, while the Face API verifies captured image by loading the Custom Dataset (Enrolled images) comparing with that of the recent before successful authentication. The LFW-TensorFlowJs combines these approaches to leverage both Secure and accurate information. Results shows that integrating LFW, TensorFlowJs and Face API model outperforms the standalone LFW.

Modern face recognition systems leverage datasets containing images of hundreds of thousands of specific individuals' faces to train deep convolutional neural networks to learn an embedding space that maps an arbitrary individual's face to a vector representation of their identity. The performance of a face recognition system in face verification (1:1) and face identification (1:N) tasks is directly related to the ability of an embedding space to discriminate between identities. Recently, there has been significant public scrutiny into the source and privacy implications of large-scale face recognition training datasets such as MS-Celeb-1M and MegaFace, as many people are uncomfortable with their face being used to train dual-use technologies that can enable mass surveillance. However, the impact of an individual's inclusion in training data on a derived system's ability to recognize them has not previously been studied. In this work, we audit ArcFace, a state-of-the-art, open source face recognition system, in a large-scale face identification experiment with more than one million distractor images. We find a Rank-1 face identification accuracy of 79.71% for individuals present in the model's training data and an accuracy of 75.73% for those not present. This modest difference in accuracy demonstrates that face recognition systems using deep learning work better for individuals they are trained on, which has serious privacy implications when one considers all major open source face recognition training datasets do not obtain informed consent from individuals during their collection.

Biometrics using biological or behavioral features to authenticate a person has attracted much attention as a new authentication approach against traditional ones such as key, password, etc. Biometrics technologies provide us better security and greater convenience than traditional person authentication technologies such as key, password and card. Biometric systems with cameras involve the risk of spoofing. For example in face recognition systems, when a malicious person turns a printed face photo of an authenticated user to a camera, a face recognition system may accept the malicious person as the authenticated user. To address the above problem, liveness detection is important to develop secure biometric recognition systems. This paper presents a liveness detection method using deep learning for face recognition systems.

Security is the main concern in any web application or apps. To ensure security, biometric systems are used for higher security system. Usually many of the security-based devices use fingerprint authentication for access. Fingerprint recognition-based devices have a large demand for security concerns. In the realm of web applications and software systems, security remains a top priority. Biometric authentication, particularly fingerprint recognition, has emerged as a reliable method for ensuring robust security. However, the need for direct fingerprint contact or passwordentry in some portals and software systems poses challenges. Authentication plays a critical role in system control and security, but the physical unavailability of individuals due to remote work or other circumstances limits the effectiveness of fingerprint-based authentication. To overcome these limitations, this paper proposes the adoption of contactless biometric systems, specifically face recognition technology, as an alternative authentication method. By leveraging face recognition, users can authenticate themselves without physical contact, offering a secure and convenient means of accessing sensitive information. This solution addresses the drawbacks of traditional fingerprint-based systems and enhances security in login systems. By embracing contactless biometric authentication methods like face recognition, organizations can not only mitigate the challenges associated with physical unavailability but also enhance security and user experience. This paper highlights the importance of incorporating face recognition technology as an alternative authentication method in login systems, providing improved security and accessibility in various applications.

Laterality effects in normal subjects' recognition of familiar faces, voices and names. Perceptual and representational components

Biometrics is the state of the art in dealing with identity identification and verification based on the physical and behavioral characteristics and widely used in the fields of Fintech, such as mobile payment and online banking due to its security and convenience. However, there are various attacks against the biometrics system. The presentation attack is one of the most common attacks that an imposter presents fake biometrics to the sensor trying to fool the system. This paper proposes a multimodal presentation attack detection (PAD) method against photo-attack and video-attack in face recognition system by using score level fusion and challenge-response scenario. The proposed challenge-response scenario is that requesting the user to speak out the randomly prompted words. Then, the recognized speech text and the user’s mouth motion are detected simultaneously to verify if the user is liveness. Two weighted score level fusion rules, namely weighted sum and weight product, are used to combine the speech and mouth motion traits as a matching score. The final score is fed into supervised machine learning algorithms and trained for classifying spoofing. The experiments are conducted in the self-built database. Experimental results show that the proposed method can achieve the best half total error rate at 3.64% and can effectively improve facial recognition system security.

Template-Protection-Techniken stellen eine wichtige Ergänzung zu biometrischen Systemen, um das gewünschte Sicherheitsniveau zu erreichen und die Anforderungen an den Schutz der Privatsphäre zu erfüllen. Diese Techniken ermöglichen Erneuerung und Widerruf der Referenzendaten in biometrischen Systemen. In dieser Arbeit wird ein systematisches Evaluierungsframework entwickelt, das sich an die wesentlichen Kriterien und Anforderungen an biometrische Template-Protection-Verfahren festhält. Seine Anwendbarkeit wird durch die Analyse der verschiedenen Algorithmen demonstriert. Die in dieser Arbeit präsentierte Evaluierung ist grundlegend für eine vollständige Analyse. Darüber hinaus ermöglicht es einen Nachweis der Sicherheit und Einhaltung der Privatsphäre. Es schafft eine Basis für Zertifizierung und Benchmark der Template-Protection-Verfahren.

In this work, we present a bimodal biometric system using speech and face features and tested its performance under degraded condition. Speaker verification (SV) system is built using Mel-Frequency Cepstral Coefficients (MFCC) followed by delta and delta-delta for feature extraction and Gaussian Mixture Model (GMM) for modeling. A face verification (FV) system is built using the combination of Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). Sum rule is used for the fusion of the biometric scores. The performance of SV system under degraded condition is also checked. All the experimental results are shown upon a subset of IITG-DIT M4 multi-biometric database. The complementary information derived from the speech biometric at training stage is used to further decrease the FV error rate, which is termed as Cohort fed FV system. Finally we propose an improved bimodal person authentication system using SV and Cohort fed FV biometric systems.

Great progress in face recognition technology has been made recently. Since the first face recognition vendor test (FRVT)Phillips et al. (2007) in 1993, face recognition performance has been improved by two orders of magnitude in thirteen years. Notably, in the FRVT 2006 it is the first time that algorithms are capable of human performance levels, and at false acceptance rates in the range of 0.05, machines can outperform humans Phillips et al. (2007). The advances are promising for face verification applications where a typical one-to-one match is performed. It is still a grand challenge to power large-scale face image retrieval. Large-scale face image retrieval is the enabling technology behind the next generation search engines (search beyondwords), bywhich web users can do social searchwith personal photos. High performance face identification algorithms are needed to support large scale face image retrieval. Compared with face verification, face identification is believed N times harder than face verification due to its nature of 1:N problems. The number of individuals N in the database has a great impact on both the effectiveness and efficiency of face identification algorithms. With the state of the art face identification algorithms, the identification rate is only around 70% (rank = 1) for the FERET database, a gallery of ten thousands individuals. When to serve for large-scale face image retrieval applications, the identification rate will further decrease as the gallery size increase (fortunately not linearly but logarithmically).The computing complexity of face identification is linearly related to the number of individuals N. For largescale face image retrieval the efficiency of face identification is a key issue. In this paper we focus on the efficiency aspects of face identification. Technically, it is very challenging to find a person from a very large or extremely large database which might hold face images of millions or hundred millions people. A highly efficient image retrieval technology is needed. Indexing technology based on tree structures has been widely used in commercial search engines. These structures are quite efficient for small dimensions (of the order of 1-10). However, as the data dimensionality increases, the query performance of these structures degrades rapidly. For instance, White and Jain report that as the dimensionality increases from 5 to 10, the performance of a nearest-neighbor query 2

Face recognition is one of the foremost applications in computer vision, which often involves sensitive signals; privacy concerns have been raised lately and tackled by several recent privacy-preserving face recognition approaches. Those systems either take advantage of information derived from the database templates or require several interaction rounds between client and server, so they cannot address outsourced scenarios. We present a private face verification system that can be executed in the server without interaction, working with encrypted feature vectors for both the templates and the probe face. We achieve this by combining two significant contributions: 1) a novel feature model for Gabor coefficients' magnitude driving a Lloyd-Max quantizer, used for reducing plaintext cardinality with no impact on performance; 2) an extension of a quasi-fully homomorphic encryption able to compute, without interaction, the soft scores of an SVM operating on quantized and encrypted parameters, features and templates. We evaluate the private verification system in terms of time and communication complexity, and in verification accuracy in widely known face databases (XM2VTS, FERET, and LFW). These contributions open the door to completely private and noninteractive outsourcing of face verification.

Nowadays, computer-based face recognition is a mature and reliable mechanism that is significantly used in many access control scenarios along with other biometric methods. Face recognition consists of two subtasks including Face Verification and Face Identification. By comparing a pair of images, Face Verification determines whether those images are related to one person or not; and Face Identification has to identify a specific face within a set of available faces in the database. There are many challenges in face recognition such as angle, illumination, pose, facial expression, noise, resolution, occlusion and the few number of one-class samples with several classes. In this paper, we are carrying out face recognition by utilizing transfer learning in a siamese network which consists of two similar CNNs. In the siamese network, a pair of two face images is given to the network as input, then the network extracts the features of this pair of images and finally, it determines whether the pair of images belongs to one person or not by using a similarity criterion. The results show that the proposed model is comparable with advanced models that are trained on datasets containing large numbers of samples. furthermore, it improves the accuracy of face recognition in comparison with methods which are trained using datasets with a few number of samples, and the mentioned accuracy is claimed to be 95.62% on LFW dataset.