A Salient Binary Coding Scheme for Face and Expression Recognition from Facial Images

Abstract

The automatic Face Expression Recognition is very important in computer vision since it has widespread applications in real time. However, the main issue is the design of an efficient descriptor that describes the appearance changes on the face. Towards such design, in this paper we have introduced a novel binary compact coding called as Edge Adaptive Local Directional Binary Pattern (EALDBP) that encodes the texture features of facial image. The proposed EALDBP is resilient to noise and illumination variations as well as it encodes the directionality of expressions. The directionality is discovered by the computation of edge responses through different compass masks. Further to encode intensity variations, we have considered the relationship between gradient pixels in the local neighborhood. Finally, a compact face descriptor is constructed with the help of Histograms computation over the encoded face image. To evaluate the performance, we tested out approach on two datasets; they are CK+ and JAFFE. The average recognition accuracy of these two datasets is observed as 90.3898% and 93.5922% for JAFFE and CK+ respectively. These accuracies demonstrate that the proposed approach outperforms the several state-of-art methods.

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