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.
With the wide applications of user authentication based on face recognition, face spoof attacks against face recognition systems are drawing increasing attentions. While emerging approaches of face antispoofing have been reported in recent years, most of them limit to the non-realistic intra-database testing scenarios instead of the cross-database testing scenarios. We propose a robust representation integrating deep texture features and face movement cue like eye-blink as countermeasures for presentation attacks like photos and replays. We learn deep texture features from both aligned facial images and whole frames, and use a frame difference based approach for eye-blink detection. A face video clip is classified as live if it is categorized as live using both cues. Cross-database testing on public-domain face databases shows that the proposed approach significantly outperforms the state-of-the-art. Temporal features is important for face anti-spoofing. Unfortunately existing methods have limitations to explore such temporal features. In this work, we propose a deep neural network architecture combining Long Short-Term Memory (LSTM) units with Convolutional Neural Networks (CNN). Our architecture works well for face anti-spoofing by utilizing the LSTM units' ability of finding long relation from its input sequences as well as extracting local and dense features through convolution operations. Our best model shows significant performance improvement over general CNN architecture (5.93 vs. 7.34 ), and hand-crafted features (5.93 vs. 10.00 ) on CASIA dataset. Face anti-spoofing is very significant to the security of face recognition. Many existing literatures focus on the study of photo attack. For the video attack, however, the related research efforts are still insufficient. In this paper, instead of extracting features from a single image, features are learned from video frames. To realize face anti-spoofing, the spatiotemporal features of continuous video frames are extracted using 3D convolution neural network (CNN) from the short video frame level. Experimental results show that the two sets of face anti-spoofing public databases, Replay-Attack and CASIA, have achieved the HTER (Half Total Error Rate) of 0.04 and 10.65 , respectively, which is better than the state-of-the-art.  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. We present a real-time liveness detection approach against photograph spoofing in face recognition, by recognizing spontaneous eyeblinks, which is a non-intrusive manner. The approach requires no extra hardware except for a generic webcamera. Eyeblink sequences often have a complex underlying structure. We formulate blink detection as inference in an undirected conditional graphical framework, and are able to learn a compact and efficient observation and transition potentials from data. For purpose of quick and accurate recognition of the blink behavior, eye closity, an easily-computed discriminative measure derived from the adaptive boosting algorithm, is developed, and then smoothly embedded into the conditional model. An extensive set of experiments are presented to show effectiveness of our approach and how it outperforms the cascaded Adaboost and HMM in task of eyeblink detection.