Facial Expression Recognition Based on Combination of Spatio-temporal and Spectral Features in Local Facial Regions

This paper presents two new approaches for facial expression recognition based on digital curvelet transform and local binary patterns from three orthogonal planes (LBP-TOP) for both still image and image sequences. The features are extracted by using the digital curvelet transform on facial regions in still image. In this approach, some sub-bands correspond to angle of facial region is used. These sub-bands consist of more frequency information. The digital curvelet coefficients and LBP-TOP are represented to combine spatio-temporal and spectral features for image sequences. The obtained results by our proposed approaches on the Cohn-Kanade facial expression database have acceptable recognition rates of 91.90% and 88.38% for still image and image sequences, respectively.