身份认证中灰度共生矩阵和小波分析的活体人脸检测算法

随着身份认证技术的广泛应用，各种假冒合法用户欺骗身份认证系统的手段不断出现。针对这一问题，本文提出了一种基于灰度共生矩阵和小波分析的活体照片人脸检测方法，该方法分析了活体人脸和照片人脸成像后在纹理上的差异性，在人脸灰度共生矩阵的基础上提取能量、熵、惯性矩和相关性四个纹理特征量；同时利用小波变换对人脸图像进行二级分解，提取高频子带系数作为特征向量训练SVM分类识别，算法在公开的数据库NUAA上进行了验证，实验结果表明该方法降低了计算复杂度，提高了检测准确率。 

Face Liveness Detection using Gray Level Co-Occurrence Matrix and Wavelets Analysis in Identity Authentication

With the wide range application of Authentication technology，All kinds of spoofing attacks occur when a person tries to masquerade system by exhibiting fake faces of an authorized client. Hence, We propose to approach the problem of face liveness detection based on gray level co-occurrence matrix(GLCM) and wavelet analysis. Our method focus on analyzing the facial image texture difference between a live person and a face print. We extract the four features -energy, entropy, moment of inertia and the correlation on the basis of GLCM .In addition ,It obtain the high frequency subbands coefficients using secondary decomposition of wavelet transform for classification recognition. Primary experiments on the publicly available NUAA photo-imposter database show that the algorithm reduce the computational complexity and improve the detection accuracy.