Cross-Centroid Ripple Pattern for Facial Expression Recognition
This work addresses facial expression recognition for applications like human-computer interaction, but it is incremental as it builds on existing feature descriptor methods.
The paper tackles facial expression recognition by proposing a new feature descriptor called Cross-Centroid Ripple Pattern (CRIP), which encodes transitional patterns to handle side views and spontaneous expressions, and it achieved better accuracy rates compared to existing state-of-the-art approaches on seven datasets with challenging conditions.
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.