Content-Independent Face Presentation Attack Detection with Directional Local Binary Pattern

Abstract

Aiming to counter photo attack and video attack in face recognition (FR) systems, a content-independent face presentation attack detection scheme based on directional local binary pattern (DLBP) is proposed. In order to minimize the influences of the image content, DLBP is proposed to investigate the noise characteristics of the facial image. By using directional difference filtering, the discrepancies between the real face and the facial artefact in terms of the consistency of adjacent pixels are effectively exploited. With the DLBP feature, the detection is accomplished by using a Softmax classifier. Experiments are done with four public benchmark databases, and the results indicate its effectiveness both in intra-database and cross-database testing.