Cross-centroid ripple pattern for facial expression recognition

Abstract

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 a 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 the ability to preserve macro and microstructural variations in an extensive region, which enables it to deal with side views and spontaneous expressions. Furthermore, gradient information between cross centroid ripples provides strength to capture 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 the 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 a better accuracy rate as compared to existing state-of-the-art approaches.