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.
