Face Anti-spoofing Databases Research Articles

Face and liveness detection with criminal identification using machine learning and image processing techniques for security system

<p>In the past, real-world photos have been used to train classifiers for face liveness identification since the related face presentation attacks (PA) and real-world images have a high degree of overlap. The use of deep convolutional neural networks (CNN) and real-world face photos together to identify the liveness of a face, however, has received very little study. A face recognition system should be able to identify real faces as well as efforts at faking utilizing printed or digital presentations. A true spoofing avoidance method involves observing facial liveness, such as eye blinking and lip movement. However, this strategy is rendered useless when defending against replay assaults that use video. The anti-spoofing technique consists of two modules: the ConvNet classifier module and the blinking eye module, which measure lip and eye movement. The results of the testing demonstrate that the developed module is capable of identifying various face spoof assaults, including those made with the use of posters, masks, or smartphones. To assess the convolutional features in this study adaptively fused from deep CNN produced face pictures and convolutional layers learned from real-world identification. Extensive tests using intra-database and cross-database scenarios on cutting-edge face anti-spoofing databases including CASIA, OULU, NUAA and replay-attack dataset demonstrate that the proposed solution methods for face liveness detection. The algorithm has a 94.30% accuracy rate.</p>

A multimodal face antispoofing method based on multifeature vision transformer and multirank fusion

SummaryFace antispoofing (FAS) is attracting increasing attention from researchers because of its important role in preventing facial recognition systems from face spoofing attacks. With the advancement of various new convolutional neural network structures and the construction of various face antispoofing databases, the deep learning‐based face antispoofing algorithm has become the main method in the FAS field. However, the generalization performance of the current multimodal face antispoofing algorithm is poor, and the recognition performance of the model on different datasets is quite different. Therefore, we design a multimodal face antispoofing framework based on a multifeature transformer (MFViT) and multirank fusion (MRF). First, we use a vision transformer structure, MFViT, for multimodal face antispoofing and a combination of modalities to capture the distinguishing characteristics in each modality. Second, we design a multidimensional multimodal fusion module, MRF, according to the various modal fusion characteristics obtained by the MFViT to fuse modal information in different dimensions more effectively. Evaluation results indicate that framework we designed achieves an average classification error rate (ACER) of 1.61% on the CASIA‐SURF dataset and an ACER of 6.5% on the CASIA‐SURF CeFA dataset.

Face anti-spoofing based on weighted neighborhood pixel difference pattern

The vulnerability of face recognition-based unlocking systems to spoofing attacks is a serious problem. With the development of science and technology, face pictures on the Internet are particularly easy to obtain and recreate. A face spoofing attack happens when someone fakes a face and tries to pass the verification of the face recognition system, which seriously threatens the security of users’ information. We propose a method based on quantifying the difference between neighborhood pixels around the center of a local region, namely, weighted neighborhood difference quantization local binary pattern (LBP), against print attacks, and video-replay attacks. The proposed method quantifies the differences between neighborhood pixels without using the center one and uses a linear weighting scheme to improve the discriminant capability of the traditional LBP. The combination of the proposed algorithm and the spatial pyramid further improves the performance of face spoofing detection. We also conducted a lot of experiments in different color spaces to illustrate the role of color in face spoofing detection. The improved method achieves better results in three challenging face anti-spoofing databases, CASIA FASD, Replay-Attack, and Replay-Mobile, respectively.

Face spoofing detection based on chromatic ED-LBP texture feature

Face spoofing detection, also known as liveness detection, is a challenging and one of the most active research areas in computer vision. In this paper, a novel texture descriptor, namely equilibrium difference local binary pattern (ED-LBP), is proposed for the representation and recognition of face texture. First, the adjacent pixels discrepancy in a facial image is fully considered and the discrepancy information is encoded into LBP. This novel texture feature extraction method has the advantage of getting more elaborate texture information without increasing the feature dimension. Second, the texture signatures in different color channels are fully investigated. More specifically, the feature histograms are calculated over each image band separately. Third, by the integration of the chromatic ED-LBP histograms and the two-level spatial pyramid, the local structure information of face is encoded in our approach which can well describe the differences between facial videos of valid users and impostors. Finally, the ED-LBP histograms from different color spaces are usually cascaded into a united feature vector which is feed into SVM for classification identification. Extensive experiments on four challenging and publicly available face anti-spoofing databases, namely CASIA FASD, Replay-Attack, Replay-Mobile, and OULU-NPU, demonstrate the effectiveness of our proposed approach. The results indicate that our methods are superior to state-of-the-art techniques and can effectively resist photo and video spoofing attacks in face recognition (FR).

Unraveling robustness of deep face anti-spoofing models against pixel attacks

In the last few decades, deep-learning-based face verification and recognition systems have had enormous success in solving complex security problems. However, it has been recently shown that such efficient frameworks are vulnerable to face-spoofing attacks, which has led researchers to build proficient anti-facial-spoofing (or liveness detection) models as an additional security layer. In response, increasingly challenging and tricky attacks have been launched to fool these anti-spoofing mechanisms. In this context, this paper presents the results of an analytical study on transfer-learning-based convolutional neural networks (CNNs) for face liveness detection and differential evolution-based adversarial attacks to evaluate the efficiency of face anti-spoofing classifiers against adversarial attacks. Specifically, experiments were conducted under different use-case scenarios on four face anti-spoofing databases to highlight practical criteria that can be used in the development of countermeasures to address face-spoofing issues.

SLNet: Stereo face liveness detection via dynamic disparity-maps and convolutional neural network

Current state-of-the-art dual camera-based face liveness detection methods utilize either hand-crafted features, such as disparity, or deep texture features to classify a live face and face Presentation Attack (PA). However, these approaches limit the effectiveness of classifiers, particularly deep Convolutional Neural Networks (CNN) to unknown face PA in adverse scenarios. In contrast to these approaches, in this paper, we show that supervising a deep CNN classifier by learning disparity features using the existing CNN layers improves the performance and robustness of CNN to unknown types of face PA. For this purpose, we propose to supervise a CNN classifier by introducing a disparity layer within CNN to learn the dynamic disparity-maps. Subsequently, the rest of the convolutional layers, following the disparity layer, in the CNN are supervised using the learned dynamic disparity-maps for face liveness detection. We further propose a new video-based stereo face anti-spoofing database with various face PA and different imaging qualities. Experiments on the proposed stereo face anti-spoofing database are performed using various test case scenarios. The experimental results indicate that our proposed system shows promising performance and has good generalization ability.

Face liveness detection using convolutional-features fusion of real and deep network generated face images

Conventionally, classifiers designed for face liveness detection are trained on real-world images, where real-face images and corresponding face presentation attacks (PA) are very much overlapped. However, a little research has been carried out in utilization of the combination of real-world face images and face images generated by deep convolutional neural networks (CNN) for face liveness detection. In this paper, we evaluate the adaptive fusion of convolutional-features learned by convolutional layers from real-world face images and deep CNN generated face images for face liveness detection. Additionally, we propose an adaptive convolutional-features fusion layer that adaptively balance the fusion of convolutional-features of real-world face images and face images generated by deep CNN during training. Our extensive experiments on the state-of-the-art face anti-spoofing databases, i.e., CASIA, OULU and Replay-Attack face anti-spoofing databases with both intra-database and cross-database scenarios indicate promising performance of the proposed method on face liveness detection compared to state-of-the-art methods.

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

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.

LiveNet: Improving features generalization for face liveness detection using convolution neural networks

Performance of face liveness detection algorithms in cross-database face liveness detection tests is one of the key issues in face-biometric based systems. Recently, Convolution Neural Networks (CNN) classifiers have shown remarkable performance in intra-database face liveness detection tests. However, a little effort has been made to improve the generalization capability of CNN classifiers for cross-database and unconstrained face liveness detection tests. In this paper, we propose an efficient strategy for training deep CNN classifiers for face liveness detection task. We utilize continuous data-randomization (like bootstrapping) in the form of small mini-batches during training CNN classifiers on small scale face anti-spoofing database. Experimental results revealed that the proposed approach reduces the training time by 18.39%, while significantly lowering the HTER by 8.28% and 14.14% in cross-database tests on CASIA-FASD and Replay-Attack database respectively as compared to state-of-the-art approaches. Additionally, the proposed approach achieves satisfactory results on intra-database and cross-database face liveness detection tests, claiming a good generality over other state-of-the-art face anti-spoofing approaches.

Dynamic Texture Recognition Using Volume Local Binary Count Patterns With an Application to 2D Face Spoofing Detection

In this paper, a local spatiotemporal descriptor, namely, the volume local binary count (VLBC), is proposed for the representation and recognition of dynamic texture. This descriptor, which is similar in spirit to the volume local binary pattern (VLBP), extracts histograms of thresholded local spatiotemporal volumes using both appearance and motion features to describe dynamic texture. Unlike VLBP using binary encoding, VLBC does not exploit the local structure information and only counts the number of 1s in the thresholded codes. Thus, VLBC can include more neighboring pixels without exponentially increasing the feature dimension as VLBP does. Furthermore, a completed version of VLBC (CVLBC) is also proposed to enhance the performance of dynamic texture recognition with additional information about local contrast and central pixel intensities. The proposed method is not only efficient to compute but also effective for dynamic texture representation. In experiments with three dynamic texture databases, namely, UCLA, DynTex, and DynTex++, the proposed method produces classification rates that are comparable to those produced by the state-of-the-art approaches. In addition to dynamic texture recognition, we propose utilizing CVLBC for 2-D face spoofing detection. As an effective spatiotemporal descriptor, CVLBC can well describe the differences between facial videos of valid users and impostors, thus achieving good performance for face spoofing detection. For comparison with other methods, the proposed method is evaluated on three face antispoofing databases: Print-Attack, Replay-Attack, and CAS Face Antispoofing. The experimental results demonstrate the effectiveness of CVLBC for 2-D face spoofing detection.

Face antispoofing based on frame difference and multilevel representation

Due to advances in technology, today’s biometric systems become vulnerable to spoof attacks made by fake faces. These attacks occur when an intruder attempts to fool an established face-based recognition system by presenting a fake face (e.g., print photo or replay attacks) in front of the camera instead of the intruder’s genuine face. For this purpose, face antispoofing has become a hot topic in face analysis literature, where several applications with antispoofing task have emerged recently. We propose a solution for distinguishing between real faces and fake ones. Our approach is based on extracting features from the difference between successive frames instead of individual frames. We also used a multilevel representation that divides the frame difference into multiple multiblocks. Different texture descriptors (local binary patterns, local phase quantization, and binarized statistical image features) have then been applied to each block. After the feature extraction step, a Fisher score is applied to sort the features in ascending order according to the associated weights. Finally, a support vector machine is used to differentiate between real and fake faces. We tested our approach on three publicly available databases: CASIA Face Antispoofing database, Replay-Attack database, and MSU Mobile Face Spoofing database. The proposed approach outperforms the other state-of-the-art methods in different media and quality metrics.

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.

Integration of image quality and motion cues for face anti-spoofing: A neural network approach

Many trait-specific countermeasures to face spoofing attacks have been developed for security of face authentication. However, there is no superior face anti-spoofing technique to deal with every kind of spoofing attack in varying scenarios. In order to improve the generalization ability of face anti-spoofing approaches, an extendable multi-cues integration framework for face anti-spoofing using a hierarchical neural network is proposed, which can fuse image quality cues and motion cues for liveness detection. Shearlet is utilized to develop an image quality-based liveness feature. Dense optical flow is utilized to extract motion-based liveness features. A bottleneck feature fusion strategy can integrate different liveness features effectively. The proposed approach was evaluated on three public face anti-spoofing databases. A half total error rate (HTER) of 0% and an equal error rate (EER) of 0% were achieved on both REPLAY-ATTACK database and 3D-MAD database. An EER of 5.83% was achieved on CASIA-FASD database.

Robust multimodal face and fingerprint fusion in the presence of spoofing attacks

Anti-spoofing is attracting growing interest in biometrics, considering the variety of fake materials and new means to attack biometric recognition systems. New unseen materials continuously challenge state-of-the-art spoofing detectors, suggesting for additional systematic approaches to target anti-spoofing. By incorporating liveness scores into the biometric fusion process, recognition accuracy can be enhanced, but traditional sum-rule based fusion algorithms are known to be highly sensitive to single spoofed instances. This paper investigates 1-median filtering as a spoofing-resistant generalised alternative to the sum-rule targeting the problem of partial multibiometric spoofing where m out of n biometric sources to be combined are attacked. Augmenting previous work, this paper investigates the dynamic detection and rejection of liveness-recognition pair outliers for spoofed samples in true multi-modal configuration with its inherent challenge of normalisation. As a further contribution, bootstrap aggregating (bagging) classifiers for fingerprint spoof-detection algorithm is presented. Experiments on the latest face video databases (Idiap Replay-Attack Database and CASIA Face Anti-Spoofing Database) and fingerprint spoofing database (Fingerprint Liveness Detection Competition 2013) illustrate the efficiency of proposed techniques.

Face liveness detection using dynamic texture

Abstract 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.