Real-time facial expression recognition based on boosted embedded hidden Markov model

The most expressive way human display emotion is through facial expressions. Facial expression recognition is necessary for designing any practical human-machine interfaces. This paper proposes a novel framework to real-time facial expression recognition within the interactive computer environment. The two major contributions of this work are: first, we proposed a novel network structure and parameters learning algorithm for embedded HMM (Ara V. Nefian and Monson H. Hayes III, March, 1999) based on AdaBoost (Freund, Y. and Schapire, R. E., 1997). Second, we apply this optimized embedded HMM to real-time facial expression recognition. In this paper, the embedded HMM uses two-dimensional discrete cosine transform (2D-DCT) coefficients as the observation vectors opposite to previous HMM approaches which use pixel intensities to form the observation vectors. Our proposed system reduces the complexity of the training and recognition system. It offers a more flexible framework and can be used in real-time human-machine interactive applications. Experimental results demonstrate that the proposed approach is an effective method to recognize facial expression.