Jukka Komulainen

Face liveness detection using dynamic texture

User authentication is an important step to protect information, and in this context, face biometrics is potentially advantageous. Face biometrics is natural, intuitive, easy to use, and less human-invasive. Unfortunately, recent work has revealed that face biometrics is vulnerable to spoofing attacks using cheap low-tech equipment. This paper introduces a novel and appealing approach to detect face spoofing using the spatiotemporal (dynamic texture) extensions of the highly popular local binary pattern operator. The key idea of the approach is to learn and detect the structure and the dynamics of the facial micro-textures that characterise real faces but not fake ones. We evaluated the approach with two publicly available databases (Replay-Attack Database and CASIA Face Anti-Spoofing Database). The results show that our approach performs better than state-of-the-art techniques following the provided evaluation protocols of each database.

The 2nd competition on counter measures to 2D face spoofing attacks

As a crucial security problem, anti-spoofing in biometrics, and particularly for the face modality, has achieved great progress in the recent years. Still, new threats arrive inform of better, more realistic and more sophisticated spoofing attacks. The objective of the 2nd Competition on Counter Measures to 2D Face Spoofing Attacks is to challenge researchers to create counter measures effectively detecting a variety of attacks. The submitted propositions are evaluated on the Replay-Attack database and the achieved results are presented in this paper.

Complementary countermeasures for detecting scenic face spoofing attacks

The face recognition community has finally started paying more attention to the long-neglected problem of spoofing attacks. The number of countermeasures is gradually increasing and fairly good results have been reported on the publicly available databases. There exists no superior antispoofing technique due to the varying nature of attack scenarios and acquisition conditions. Therefore, it is important to find out complementary countermeasures and study how they should be combined in order to construct an easily extensible anti-spoofing framework. In this paper, we address this issue by studying fusion of motion and texture based countermeasures under several types of scenic face attacks. We provide an intuitive way to explore the fusion potential of different visual cues and show that the performance of the individual methods can be vastly improved by performing fusion at score level. The Half-Total Error Rate (HTER) of the best individual countermeasure was decreased from 11.2% to 5.1% on the Replay Attack Database. More importantly, we question the idea of using complex classification schemes in individual countermeasures, since nearly same fusion performance is obtained by replacing them with a simple linear one. In this manner, the computational efficiency and also probably the generalization ability of the resulting anti-spoofing framework are increased.

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.

Context based face anti-spoofing

The face recognition community has finally started paying more attention to the long-neglected problem of spoofing attacks and the number of countermeasures is gradually increasing. Fairly good results have been reported on the publicly available databases but it is reasonable to assume that there exists no superior anti-spoofing technique due to the varying nature of attack scenarios and acquisition conditions. Therefore, we propose to approach the problem of face spoofing as a set of attack-specific subproblems that are solvable with a proper combination of complementary countermeasures. Inspired by how we humans can perform reliable spoofing detection only based on the available scene and context information, this work provides the first investigation in research literature that attempts to detect the presence of spoofing medium in the observed scene. We experiment with two publicly available databases consisting of several fake face attacks of different nature under varying conditions and imaging qualities. The experiments show excellent results beyond the state of the art. More importantly, our cross-database evaluation depicts that the proposed approach has promising generalization capabilities.

Face spoofing detection using dynamic texture

While there is a significant number of works addressing e.g. pose and illumination variation problems in face recognition, the vulnerabilities to spoofing attacks were mostly unexplored until very recently when an increasing attention is started to be paid to this threat. A spoofing attack occurs when a person tries to masquerade as someone else e.g. by wearing a mask to gain illegitimate access and advantages. This work provides the first investigation in research literature on the use of dynamic texture for face spoofing detection. Unlike masks and 3D head models, real faces are indeed non-rigid objects with contractions of facial muscles which result in temporally deformed facial features such as eye lids and lips. Our key idea is to learn the structure and the dynamics of the facial micro-textures that characterise only real faces but not fake ones. Hence, we introduce a novel and appealing approach to face spoofing detection using the spatiotemporal (dynamic texture) extensions of the highly popular local binary pattern approach. We experiment with two publicly available databases consisting of several fake face attacks of different natures under varying conditions and imaging qualities. The experiments show excellent results beyond the state-of-the-art.

Generalized textured contact lens detection by extracting BSIF description from Cartesian iris images

Textured contact lenses cause severe problems for iris biometric systems because they can be used to alter the appearance of iris texture in order to deliberately increase the false positive and, especially, false negative match rates. Many texture analysis based techniques have been proposed for detecting the presence of cosmetic contact lenses. However, it has been shown recently that the generalization capability of the existing approaches is not sufficient because they have been developed for detecting specific lens texture patterns and evaluated only on those same lens types seen during development phase. This scenario does not apply in unpredictable practical applications because unseen lens patterns will be definitely experienced in operation. In this paper, we address this issue by studying the effect of different iris image preprocessing techniques and introducing a novel approach formore generalized cosmetic contact lens detection using binarized statistical image features (BSIF).Our extensive experimental analysis on benchmark datasets shows that the BSIF description extracted from preprocessed Cartesian iris texture images yields to promising generalization capabilities across unseen texture patterns and different iris sensors with mean equal error rate of 0.14%and 0.88%, respectively. The findings support the intuition that the textural differences between genuine iris texture and fake ones are best described by preserving the regular structure of different printing signatures without transforming the iris images into polar coordinate system.

OULU-NPU: A Mobile Face Presentation Attack Database with Real-World Variations

The vulnerabilities of face-based biometric systems to presentation attacks have been finally recognized but yet we lack generalized software-based face presentation attack detection (PAD) methods performing robustly in practical mobile authentication scenarios. This is mainly due to the fact that the existing public face PAD datasets are beginning to cover a variety of attack scenarios and acquisition conditions but their standard evaluation protocols do not encourage researchers to assess the generalization capabilities of their methods across these variations. In this present work, we introduce a new public face PAD database, OULU-NPU, aiming at evaluating the generalization of PAD methods in more realistic mobile authentication scenarios across three covariates: unknown environmental conditions (namely illumination and background scene), acquisition devices and presentation attack instruments (PAI). This publicly available database consists of 5940 videos corresponding to 55 subjects recorded in three different environments using high-resolution frontal cameras of six different smartphones. The high-quality print and videoreplay attacks were created using two different printers and two different display devices. Each of the four unambiguously defined evaluation protocols introduces at least one previously unseen condition to the test set, which enables a fair comparison on the generalization capabilities between new and existing approaches. The baseline results using color texture analysis based face PAD method demonstrate the challenging nature of the database.

Face Anti-spoofing: Visual Approach

User authentication is an important step to protect information and in this regard face biometrics is advantageous. Face biometrics is natural, easy to use and less human-invasive. Unfortunately, recent work revealed that face biometrics is quite vulnerable to spoofing attacks. This chapter presents the different modalities of attacks to visual spectrum face recognition systems. We introduce public datasets for the evaluation of vulnerability of recognition systems and performance of countermeasures. Finally, we build a comprehensive view of antispoofing techniques for visual spectrum face recognition and provides an outlook of issues that remain unaddressed.