关键词: 人脸检测, 人脸对齐, 活体检测, 支持向量机, 交互式随机动作

Abstract: Aiming at the problem of “cheating” unlocking through photos or videos in the face recognition system, a live detection method is proposed to judge whether the detected object is a real person through random instructions. In this paper, HOG feature and random forest algorithm are used to detect the face of the image collected by the camera, and the 68 feature points of the face are predicted. The 68 feature point coordinates are normalized, and in combination with the face position, the resulting relative position coordinates are used as facial gesture features. The posture features and SVM classifier combine to train the head posture estimation classifier with good classification effect. Through several experiments, the feature extraction method is optimized to further improve the detection accuracy. Finally, a random interactive command sequence is used to instruct the person to be tested to perform live detection. This method has higher robustness to head pose estimation and can effectively prevent photo and video authentication attacks.

Key words: face detection, face alignment, living body detection, support vector machine, interactive random action