Zinelabidine Boulkenafet

Face Spoofing Detection Using Colour Texture Analysis

Research on non-intrusive software-based face spoofing detection schemes has been mainly focused on the analysis of the luminance information of the face images, hence discarding the chroma component, which can be very useful for discriminating fake faces from genuine ones. This paper introduces a novel and appealing approach for detecting face spoofing using a colour texture analysis. We exploit the joint colour-texture information from the luminance and the chrominance channels by extracting complementary low-level feature descriptions from different colour spaces. More specifically, the feature histograms are computed over each image band separately. Extensive experiments on the three most challenging benchmark data sets, namely, the CASIA face anti-spoofing database, the replay-attack database, and the MSU mobile face spoof database, showed excellent results compared with the state of the art. More importantly, unlike most of the methods proposed in the literature, our proposed approach is able to achieve stable performance across all the three benchmark data sets. The promising results of our cross-database evaluation suggest that the facial colour texture representation is more stable in unknown conditions compared with its gray-scale counterparts.

face anti-spoofing based on color texture analysis

Research on face spoofing detection has mainly been focused on analyzing the luminance of the face images, hence discarding the chrominance information which can be useful for discriminating fake faces from genuine ones. In this work, we propose a new face anti-spoofing method based on color texture analysis. We analyze the joint color-texture information from the luminance and the chrominance channels using a color local binary pattern descriptor. More specifically, the feature histograms are extracted from each image band separately. Extensive experiments on two benchmark datasets, namely CASIA face anti-spoofing and Replay-Attack databases, showed excellent results compared to the state-of-the-art. Most importantly, our inter-database evaluation depicts that the proposed approach showed very promising generalization capabilities.

An original face anti-spoofing approach using partial convolutional neural network

Recently deep Convolutional Neural Networks have been successfully applied in many computer vision tasks and achieved promising results. So some works have introduced the deep learning into face anti-spoofing. However, most approaches just use the final fully-connected layer to distinguish the real and fake faces. Inspired by the idea of each convolutional kernel can be regarded as a part filter, we extract the deep partial features from the convolutional neural network (CNN) to distinguish the real and fake faces. In our prosed approach, the CNN is fine-tuned firstly on the face spoofing datasets. Then, the block principle component analysis (PCA) method is utilized to reduce the dimensionality of features that can avoid the over-fitting problem. Lastly, the support vector machine (SVM) is employed to distinguish the real the real and fake faces. The experiments evaluated on two public available databases, Replay-Attack and CASIA, show the proposed method can obtain satisfactory results compared to the state-of-the-art methods.

Face Antispoofing Using Speeded-Up Robust Features and Fisher Vector Encoding

The vulnerabilities of face biometric authentication systems to spoofing attacks have received a significant attention during the recent years. Some of the proposed countermeasures have achieved impressive results when evaluated on intratests, i.e., the system is trained and tested on the same database. Unfortunately, most of these techniques fail to generalize well to unseen attacks, e.g., when the system is trained on one database and then evaluated on another database. This is a major concern in biometric antispoofing research that is mostly overlooked. In this letter, we propose a novel solution based on describing the facial appearance by applying Fisher vector encoding on speeded-up robust features extracted from different color spaces. The evaluation of our countermeasure on three challenging benchmark face-spoofing databases, namely the CASIA face antispoofing database, the replay-attack database, and MSU mobile face spoof database, showed excellent and stable performance across all the three datasets. Most importantly, in interdatabase tests, our proposed approach outperforms the state of the art and yields very promising generalization capabilities, even when only limited training data are used.

Scale space texture analysis for face anti-spoofing

Face spoofing detection (i.e. face anti-spoofing) is emerging as a new research area and has already attracted a good number of works during the past five years. This paper addresses for the first time the key problem of the variation in the input image quality and resolution in face anti-spoofing. In contrast to most existing works aiming at extracting multiscale descriptors from the original face images, we derive a new multiscale space to represent the face images before texture feature extraction. The new multiscale space representation is derived through multiscale filtering. Three multiscale filtering methods are considered including Gaussian scale space, Difference of Gaussian scale space and Multiscale Retinex. Extensive experiments on three challenging and publicly available face anti-spoofing databases demonstrate the effectiveness of our proposed multiscale space representation in improving the performance of face spoofing detection based on gray-scale and color texture descriptors.

Face Anti-spoofing in Biometric Systems

Despite the great deal of progress in face recognition, current systems are vulnerable to spoofing attacks. Several anti-spoofing methods have been proposed to determine whether there is a live person or an artificial replica in front of the camera of face recognition system. Yet, developing efficient protection methods against this threat has proven to be a challenging task. In this chapter, we present a comprehensive overview of the state-of-the-art in face spoofing and anti-spoofing, describing existing methodologies, their pros and cons, results and databases. Moreover, after a comprehensive review of the available literature in the field, we present a new face anti-spoofing method based on color texture analysis, which analyzes the joint color-texture information from the luminance and the chrominance channels using color local binary pattern descriptor. The experiments on two challenging spoofing database exhibited excellent results. In particular, in inter-database evaluation, the proposed approach showed very promising generalization capabilities. We hope this case study stimulates the development of generalized face liveness detection. Lastly, we point out some of the potential research directions in face anti-spoofing.

Audiovisual synchrony assessment for replay attack detection in talking face biometrics

Audiovisual speech synchrony detection is an important liveness check for talking face verification systems in order to make sure that the input biometric samples are actually acquired from the same source. In prior work, the used visual speech features have been mainly describing facial appearance or mouth shape in frame-wise manner, thus ignoring the lip motion between consecutive frames. Since also the visual speech dynamics are important, we take the spatiotemporal information into account and propose the use of space-time auto-correlation of gradients (STACOG) for measuring the audiovisual synchrony. For evaluating the effectiveness of the proposed approach, a set of challenging and realistic attack scenarios are designed by augmenting publicly available BANCA and XM2VTS datasets with synthetic replay attacks. Our experimental analysis shows that the STACOG features outperform the state of the art, e.g. discrete cosine transform based features, in measuring the audiovisual synchrony.

On the generalization of color texture-based face anti-spoofing

Abstract Despite the significant attention given to the problem of face spoofing, we still lack generalized presentation attack detection (PAD) methods performing robustly in practical face recognition systems. The existing face anti-spoofing techniques have indeed achieved impressive results when trained and evaluated on the same database (i.e. intra-test protocols). Cross-database experiments have, however, revealed that the performance of the state-of-the-art methods drops drastically as they fail to cope with new attacks scenarios and other operating conditions that have not been seen during training and development phases. So far, even the popular convolutional neural networks (CNN) have failed to derive well-generalizing features for face anti-spoofing. In this work, we explore the effect of different factors, such as acquisition conditions and presentation attack instrument (PAI) variation, on the generalization of color texture-based face anti-spoofing. Our extensive cross-database evaluation of seven color texture-based methods demonstrates that most of the methods are unable to generalize to unseen spoofing attack scenarios. More importantly, the experiments show that some facial color texture representations are more robust to particular PAIs than others. From this observation, we propose a face PAD solution of attack-specific countermeasures based solely on color texture analysis and investigate how well it generalizes under display and print attacks in different conditions. The evaluation of the method combining attack-specific detectors on three benchmark face anti-spoofing databases showed remarkable generalization ability against display attacks while print attacks require still further attention.

Face Verification Based on Gabor Region Covariance Matrices

This paper introduces a novel face verification approach using the Gabor Region Covariance Matrices (GRCM). First, we represent the face images with \(d\) dimensional Gabor images. Then, we divide these images into overlapping regions. From each region, we compute a \(d\times d\) covariance matrix. Inspired by the GMM-UBM speaker verification framework, we propose a new decision rule based on the Riemannian mean of the Gabor region covariance matrices computed from background faces. Finally, score normalization techniques are incorporated in the proposed framework to enhance the verification performance. Extensive experiments on two benchmark databases, namely Banca and SCface showed very interesting results which compare favorably against many state-of-the-art methods.

Forensic evidence reporting using GMM-UBM, JFA and I-vector methods: Application to Algerian Arabic dialect

Nowadays, under controlled conditions the speaker verification systems based on the GMM-UBM paradigm show very good performance. However, in forensic investigation activities the conditions; in which recordings are acquired; are uncontrollable, a naive use of the baseline GMM-UBM system without feature normalization, model transformation and score normalization techniques yields to unreliable forensic reporting. In this paper, we investigate forensic reporting using corpus-based likelihood ratio evaluation; which gained popularity in recent years; using two state-of-the-art speaker recognition systems: The JFA system which models explicitly the speaker and session variability during training stage and the I-vector paradigm which models the total variability and use compensation techniques to handle session mismatch. The GMM-UBM, Joint Factor Analysis and I-vector systems are compared in verification performance using Half Total Error Rates (HTER) and in forensic reporting using TIPPET plots. Experimental results on an Algerian Arabic dialect under different telephonic recording conditions confirm the robustness of I-vector and JFA systems in handling cross-channel mismatch and highlight clearly the drastic deterioration of the performance of the GMM-UBM system.