优化特征提取的互动式人脸活体检测研究  

Research on Interactive Face Detection Based on Optimized Feature Extraction

作　　者：张进 张娜娜[2] ZHANG Jin;ZHANG Nana(Information Institute,Shanghai Ocean University,Shanghai 201306,China;Information Technology Institute,Shanghai Jian Qiao University,Shanghai 201306,China)

机构地区：[1]上海海洋大学信息学院,上海201306 [2]上海建桥学院信息技术学院,上海201306

出　　处：《计算机工程与应用》2019年第13期193-200,共8页Computer Engineering and Applications

基　　金：上海市教育委员会“晨光计划”基金(No.AASH1702)

摘　　要：针对人脸识别系统中出现的通过照片或视频“欺诈”解锁问题,提出一种活体检测方法,通过随机指令判断被检测对象是否为真人。利用HOG特征和随机森林算法对摄像头采集的图像进行人脸检测,预测脸部68个特征点位置,把68个特征点位置和脸部位置的相对位置归一化后作为姿态的特征,提取的姿态特征与SVM分类器相结合,训练出分类效果较好的头部姿态估计分类器。通过多次实验,对特征提取方法进行优化,进一步提高检测准确率。最后,使用随机互动式命令序列对被检测人发出指令,实现活体检测。该方法对头部姿态估计具有较高的鲁棒性,并且可以有效地防范照片和视频认证攻击。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.

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 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]