Improving Face Recognition Systems Security Using Local Binary Patterns

Face recognition systems suffer from different spoofing attacks like photograph-based and video-based spoofing attacks. This paper presents a new method to empower the face recognition systems against video-based spoofing by employing efficient scene texture analyzing. To this end, the scene of input and reference images are divided into same non-overlapped blocks and the texture pattern of each block is extracted by local binary pattern (LBP) operator. To reduce the sensitivity of LBP to noise and also to increase the reliability of the proposed method, first input image transformed to YCbCr color space and then similarity of texture pattern in Y, Cb and Cr channels are extracted independently. The majority of similarity of three channels is used as the final similarity of each block. The ratio of same blocks in the input image and reference image is used as a measure for detecting video-based spoofing attacks. The performance of the proposed algorithm is evaluated using several scenarios. The obtained results show the effectiveness of the proposed algorithm against video-based spoofing attacks in real environments.