Local binary pattern and its derivatives for face recognition

Abstract

Texture is the surface property that is used to identify and recognise objects. This property is widely used in many applications including texture-based face recognition systems, surveillance, identity verification and so on. The Local binary pattern (LBP) texture method is most successful for face recognition. Owing to the great success of LBP, recently many models, which are variants of LBP have been proposed for texture analysis. Some of the derivatives of LBPs are multivariate local binary pattern, centre symmetric local binary pattern, local binary pattern variance, dominant local binary pattern, advanced local binary pattern, local texture pattern (LTP) and local derivative pattern (LDP). In this scenario, it is essential to review, whether LBP or their derivatives perform better for face recognition. The real-time challenges such as illumination changes, rotations, angle variations and facial expression variations are evaluated by different LBP-based models. Experiments were conducted on the Japanese female facial expression, YALE and FRGC version2 databases. The results show that LDP and LTP perform much better than the other LBP-based models.