Regional adaptive affinitive patterns (RADAP) with logical operators for facial expression recognition

Abstract

Automated facial expression recognition plays a significant role in the study of human behaviour analysis. In this study, the authors propose a robust feature descriptor named regional adaptive affinitive patterns (RADAP) for facial expression recognition. The RADAP computes positional adaptive thresholds in the local neighbourhood and encodes multi-distance magnitude features which are robust to intra-class variations and irregular illumination variation in an image. Furthermore, they established cross-distance co-occurrence relations in RADAP by using logical operators. They proposed XRADAP, ARADAP, and DRADAP using xor, adder and decoder, respectively. The XRADAP engrains the quality of robustness to intra-class variations in RADAP features using pairwise co-occurrence. Similarly, ARADAP and DRADAP extract more stable and illumination invariant features and capture the minute expression features which are usually missed by regular descriptors. The performance of the proposed methods is evaluated by conducting experiments on nine benchmark datasets Cohn–Kanade+ (CK+), Japanese female facial expression (JAFFE), Multimedia Understanding Group (MUG), MMI, OULU-CASIA, Indian spontaneous expression database, DISFA, AFEW and Combined (CK+, JAFFE, MUG, MMI & GEMEP-FERA) database in both person dependent and person independent setup. The experimental results demonstrate the effectiveness of the proposed method over state-of-the-art approaches.