Face anti-spoofing is significant to the security of face recognition systems. Previous works on depth supervised learning have proved the effectiveness for face anti-spoofing. Nevertheless, they only considered the depth as an auxiliary supervision in the single frame. Different from these methods, we develop a new method to estimate depth information from multiple RGB frames and propose a depth-supervised architecture which can efficiently encodes spatiotemporal information for presentation attack detection. It includes two novel modules: optical flow guided feature block (OFFB) and convolution gated recurrent units (ConvGRU) module, which are designed to extract short-term and long-term motion to discriminate living and spoofing faces. Extensive experiments demonstrate that the proposed approach achieves state-of-the-art results on four benchmark datasets, namely OULU-NPU, SiW, CASIA-MFSD, and Replay-Attack.
Since face anti-spoofing is essentially a binary classification problem, most of previous anti-spoofing methods purely train a classifier under binary supervision, e.g., spoofing face as 0 and living face as 1. Binary classifiers contain traditional classifiers and neural networks. Prior works usually rely on hand-crafted features, such as LBP @cite_27 @cite_39 @cite_8 , SIFT @cite_4 , SURF @cite_31 , HoG @cite_1 @cite_5 , DoG @cite_33 @cite_28 with traditional classifiers, such as SVM and Random Forest. Since these manually-engineered features are often sensitive to varying condition, such as camera devices, lighting conditions and presentation attack instruments (PAIs), traditional methods often perform poorly in generalization.
@cite_4 @cite_33 @cite_8 @cite_28 @cite_1 @cite_39 @cite_27 @cite_5 @cite_31
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A multi-cues integration framework is proposed using a hierarchical neural network.Bottleneck representations are effective in multi-cues feature fusion.Shearlet is utilized to perform face image quality assessment.Motion-based face liveness features are automatically learned using autoencoders. 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. Though having achieved some progresses, the hand-crafted texture features, e.g., LBP [23], LBP-TOP [11] are still unable to capture the most discriminative cues between genuine and fake faces. In this paper, instead of designing feature by ourselves, we rely on the deep convolutional neural network (CNN) to learn features of high discriminative ability in a supervised manner. Combined with some data pre-processing, the face anti-spoofing performance improves drastically. In the experiments, over 70 relative decrease of Half Total Error Rate (HTER) is achieved on two challenging datasets, CASIA [36] and REPLAY-ATTACK [7] compared with the state-of-the-art. Meanwhile, the experimental results from inter-tests between two datasets indicates CNN can obtain features with better generalization ability. Moreover, the nets trained using combined data from two datasets have less biases between two datasets. We present a novel approach to address the representation issue and the matching issue in face recognition (verification). Firstly, our approach encodes the micro-structures of the face by a new learning-based encoding method. Unlike many previous manually designed encoding methods (e.g., LBP or SIFT), we use unsupervised learning techniques to learn an encoder from the training examples, which can automatically achieve very good tradeoff between discriminative power and invariance. Then we apply PCA to get a compact face descriptor. We find that a simple normalization mechanism after PCA can further improve the discriminative ability of the descriptor. The resulting face representation, learning-based (LE) descriptor, is compact, highly discriminative, and easy-to-extract. To handle the large pose variation in real-life scenarios, we propose a pose-adaptive matching method that uses pose-specific classifiers to deal with different pose combinations (e.g., frontal v.s. frontal, frontal v.s. left) of the matching face pair. Our approach is comparable with the state-of-the-art methods on the Labeled Face in Wild (LFW) benchmark (we achieved 84.45 recognition rate), while maintaining excellent compactness, simplicity, and generalization ability across different datasets. Spoofing using photographs or videos is one of the most common methods of attacking face recognition and verification systems. In this paper, we propose a real-time and nonintrusive method based on the diffusion speed of a single image to address this problem. In particular, inspired by the observation that the difference in surface properties between a live face and a fake one is efficiently revealed in the diffusion speed, we exploit antispoofing features by utilizing the total variation flow scheme. More specifically, we propose defining the local patterns of the diffusion speed, the so-called local speed patterns, as our features, which are input into the linear SVM classifier to determine whether the given face is fake or not. One important advantage of the proposed method is that, in contrast to previous approaches, it accurately identifies diverse malicious attacks regardless of the medium of the image, e.g., paper or screen. Moreover, the proposed method does not require any specific user action. Experimental results on various data sets show that the proposed method is effective for face liveness detection as compared with previous approaches proposed in studies in the literature.