Facial expression recognition via deep learning

This paper mainly studies facial expression recognition with the components by face parsing (FP). Considering the disadvantage that different parts of face contain different amount of information for facial expression and the weighted function are not the same for different faces, an idea is proposed to recognize facial expression using components which are active in expression disclosure. The face parsing detectors are trained via deep belief network and tuned by logistic regression. The detectors first detect face, and then detect nose, eyes and mouth hierarchically. A deep architecture pretrained with stacked autoencoder is applied to facial expression recognition with the concentrated features of detected components. The parsing components remove the redundant information in expression recognition, and images don't need to be aligned or any other artificial treatment. Experimental results on the Japanese Female Facial Expression database and extended Cohn-Kanade dataset outperform other methods and show the effectiveness and robustness of this algorithm.