  In this paper, we propose a new feature descriptor Cross-Centroid Ripple
Pattern (CRIP) for facial expression recognition. CRIP encodes the transitional
pattern of a facial expression by incorporating cross-centroid relationship
between two ripples located at radius r1 and r2 respectively. These ripples are
generated by dividing the local neighborhood region into subregions. Thus, CRIP
has ability to preserve macro and micro structural variations in an extensive
region, which enables it to deal with side views and spontaneous expressions.
Furthermore, gradient information between cross centroid ripples provides
strenght to captures prominent edge features in active patches: eyes, nose and
mouth, that define the disparities between different facial expressions. Cross
centroid information also provides robustness to irregular illumination.
Moreover, CRIP utilizes the averaging behavior of pixels at subregions that
yields robustness to deal with noisy conditions. The performance of proposed
descriptor is evaluated on seven comprehensive expression datasets consisting
of challenging conditions such as age, pose, ethnicity and illumination
variations. The experimental results show that our descriptor consistently
achieved better accuracy rate as compared to existing state-of-art approaches.
