A novel phase congruency based descriptor for dynamic facial expression analysis

Representation and classification of dynamic visual events in videos have been an active field of research. This work proposes a novel spatio-temporal descriptor based on phase congruency concept and applied it to recognize facial expression from video sequences. The proposed descriptor comprises histograms of dominant phase congruency over multiple 3D orientations to describe both spatial and temporal information of a dynamic event. The advantages of our proposed approach are local and dynamic processing, high accuracy, robustness to image scale variation, and illumination changes. We validated the performance of our proposed approach using the Cohn-Kanade (CK +) database where we achieved 95.44% accuracy in detecting six basic emotions. We also validated its robustness to illumination and scale variation using our own collected dataset.