Local features based facial expression recognition with face registration errors

Tommaso Gritti, Caifeng Shan, Vincent Jeanne, Ralph Braspenning

Abstract: In this paper, we extensively investigate local features based facial expression recognition with face registration errors, which has never been addressed before. Our contributions are three fold. Firstly, we propose and experimentally study the histogram of oriented gradients (HOG) descriptors for facial representation. Secondly, we present facial representations based on local binary patterns (LBP) and local ternary patterns (LTP) extracted from overlapping local regions. Thirdly, we quantitatively study the impact of face registration errors on facial expression recognition using different facial representations. Overall LBP with overlapping gives the best performance (92.9% recognition rate on the Cohn-Kanade database), while maintaining a compact feature vector and best robustness against face registration errors.