面向活体人脸检测的时空纹理特征级联方法

Spatial-Temporal Texture Cascaded Feature Method for Face Liveness Detection

为了解决身份认证中的安全问题,通常采用活体人脸检测方法.为提高活体人脸检测的鲁棒性,文中提出时空纹理特征级联方法.首先采用局部二值模式(LBP)计算韦伯局部描述符(WLD)的差分激励,利用Prewitt算子计算WLD的方向角,提取时空域的纹理特征.再将3个正交时空平面XY、XT、YT的纹理特征直方图进行级联,得到动态纹理特征即时空纹理级联特征,并对真实人脸和伪装人脸做出判定.在公开活体人脸数据库上的实验表明,相比现有主流局部纹理特征方法,文中方法识别率更高.

To solve the security problem in identity authentication,the face liveness detection method is always employed.Therefore,a spatial-temporal texture cascaded feature method is proposed to improve the robustness of living face detection.Firstly,local binary pattern(LBP)is utilized to calculate the differential excitation of Weber local descriptor(WLD),and Prewitt operator is exploited to calculate the directional angle of WLD to extract texture features in time domain and space domain.Secondly,the histogram of texture features obtained from three orthogonal space-time planes,XY,XT and YT,is cascaded.Finally,the dynamic texture features,namely spatial-temporal texture cascade features,can be used to determine whether the real face or the disguised face.Experimental results on CASIA face anti-spoofing database and replay-attack database show that the proposed method obtains higher recognition rate than the existing mainstream local texture feature methods and it can be widely used in identity authentication and security monitoring systems.