Color channel-wise recurrent learning for facial expression recognition
Facial expression recognition is increasingly gaining importance in emerging affective computing applications. In practice, achieving accurate facial expression recognition is still challenging due to environmental variations. In this paper, we propose a color channel-wise recurrent facial feature learning. The proposed method adopts recurrent neural network to learn expression features sequentially along color channels. The proposed network preserves discriminative expression feature through a long short-term memory for the sequence of color spatial features. Comprehensive experiments have been conducted on the publically available CMU Multi-PIE dataset under illumination variations. Experimental results showed that the proposed method achieved higher recognition rates compared to the state-of-the-art methods.