基于近红外与可见光双目视觉的活体人脸检测方法

针对人脸识别系统易受伪造攻击的问题，提出了一种基于近红外与可见光双目视觉的活体人脸检测方法。首先，采用近红外与可见光双目装置同步获取人脸图像，提取两图像的人脸特征点，利用双目关系实现特征点的匹配并获取其深度信息，再利用深度信息进行三维点云重建；然后，将全部人脸特征点划分为四个区域，计算各区域内人脸特征点在深度方向的平均方差；接着，选取人脸关键特征点，以鼻尖点为参照点，计算鼻尖点到人脸关键特征点之间的空间距离；最后，利用人脸特征点的深度值方差和空间距离来构造特征向量，使用支持向量机（SVM）实现活体人脸判断。实验结果表明，所提方法能够准确检测活体人脸以及有效抵御伪造人脸的攻击，在实验测试中达到99.0%的识别率，在准确性和鲁棒性上优于利用人脸特征点深度信息进行检测的同类算法。

Face liveness detection method based on near-infrared and visible binocular vision

Aiming at the problem that face recognition systems are suspectable to be affected by forgery attacks, a face liveness detection method based on near-infrared and visible binocular vision was proposed. Firstly, the binocular device was used to obtain the face images of near-infrared and visible light synchronously. Then, the facial feature points of two images were extracted, and the binocular relation was used to match the feature points and obtain their depth information, which was used for three-dimensional point cloud reconstruction. Secondly, all facial feature points were divided into four regions, and the average variance of facial feature points in the depth direction within each region was calculated. Thirdly, the key feature points of face were selected. With the nasal tip point as the reference point, the spatial distances between the nasal tip point and the key feature points were calculated. Finally, the feature vectors were constructed by using the depth value variances and spatial distances of facial feature points. And Support Vector Machine (SVM) was used for the judgment of real faces. The experimental results show that the proposed method can detect real faces accurately and resist the attacks of fake faces effectively, achieves the recognition rate of 99.0% in experimental tests, and is superior in accuracy and robustness to the similar algorithm using depth information of facial feature points for detection.