目的 随着人脸识别系统应用的日益广泛，提高身份认证的安全性，提升人脸活体检测的有效性已经成为迫切需要解决的问题。针对活体检测中真实用户的照片存在的人脸欺骗问题，提出一种新的解决照片攻击的人脸活体检测算法。方法 利用局部二值模式LBP（local binary pattern）、TV-L1（total variation regularization and the robust L1 norm）光流法、光学应变和深度网络实现的人脸活体检测方法。对原始数据进行预处理得到LBP特征图；对LBP特征图提取光流信息，提高对噪声适应的鲁棒性；计算光流的导数得到图像的光学应变图，以表征相邻两帧之间的微纹理性质的微小移动量；通过卷积神经网络模型（CNN）将每个应变图编码成特征向量，最终将特征向量传递给长短期记忆LSTM（long short term memory）模型进行分类，实现真假人脸的判别。结果 实验在两个公开的人脸活体检测数据库上进行，并将本文算法与具有代表性的活体检测算法进行对比。在南京航空航天大学（NUAA）人脸活体检测数据库中，算法精度达到99.79%；在Replay-attack数据库中，算法精度达到98.2%，对比实验的结果证明本文算法对照片攻击的识别更加准确。结论 本文提出的针对照片攻击的人脸活体检测算法，融合光学应变图像和深度学习模型的优点，使得人脸活体检测更加准确。

Objective Increasing application of face recognition systems improves the security of identity authentication systems and the effectiveness of face detection has become an urgent problem. In recent years, the development of face recognition has the advantages that users do not require to cooperate with the recognition equipment, and can recognize face images in a timely manner, with moderate cost, stable security, and intuitive results, thereby making face recognition a widely used technology. Thus, among all biometric features that can achieve spoofing attacks, face spoofing attacks is the first to bear the brunt. An illegal visitor can easily obtain photos of legitimate users in multiple ways, which poses a serious threat to the security system of legitimate users. Therefore, the detection of face anti-spoofing, reduction of threats to face anti-spoofing, and assurance of security of the recognition system are urgent problems to be solved. This paper proposes a novel face detection algorithm to perform photo anti-spoofing. Method According to the single difference clue between images to solve the face anti-spoofing, the algorithm has a problem of low universality. The face anti-spoofing method proposed in this paper combines three differential cues, namely, facial micro-texture change, optical strain feature map, and depth feature network. The entire experimental process combines the micro-texture information analysis method of the image, life information analysis method, and deep learning method, and divides the entire experimental flowchart into local binary patterns (LBP) image local texture feature operator to extract an LBP feature map. The total variation regularization and the robust L1 norm TV-L1 optical flow method extracts image optical flow information, and the optical strain feature describes small changes in the adjacent image frame motion and deep network extraction features, which are eventually classified into four parts. The specific steps of algorithm implementation are described in the following. First, the selected NUAA dataset and Replay-attack dataset are processed into a group of data every 10 frames. After face feature points are located in Dlib, Face++ API is used to extract facial landmarks for face alignment and crop as grayscale images to mask the effect of light on the image recognition. The LBP feature extraction operation is conducted on the cropped grayscale image to obtain the LBP feature map, which can effectively describe the image spatial information. Second, optical flow information is extracted from the LBP feature map to improve the robustness of noise adaptation, and then the derivative of the optical flow is calculated to obtain the optical strain map of the image, thereby characterizing the small amount of movement of the micro-texture properties between successive frames. Finally, convolutional neural network model (CNN) is used to encode each strain map into feature vector to extract the spatial information of the strain image, and then through the feature vector to the long short term memory (LSTM) model to learn the sequential information of the continuous image and perform classified prediction to discriminate between photo attacks used by legitimate or illegal users. Result The experiments are performed on two public human face anti-spoofing databases and compared with the representative algorithm. This paper mainly focuses on the face anti-spoofing detection algorithm for photo spoofing attacks. Therefore, the sample part of the database related to photo attacks is selected as a negative sample of experimental data, and the real face is used as a positive sample. According to the analysis of the experimental results, the NUAA database results show that the accuracy of the proposed algorithm is 99.79% in this study. Compared with the second detection method based on CNN, the algorithm has an accuracy rate that is improved by approximately 1.5%. The experimental results of the Replay-attack database show that the accuracy of our method is 98.2%. The experimental comparison results of our algorithm outperform the state of the art in identifying photo attacks. Conclusion The optical strain maps are used to effectively represent the dynamic spatiotemporal information between frames and these maps are used as the input data to represent the spatial features at time t by encoding them as a fixed length vector using CNN. Thus, the vector is transmitted into LSTM to learn the temporal dynamic information features of the face detection algorithm for photo attack. The face anti-spoofing detection is more accurate when the advantages of the optical strain image and deep learning model are integrated.