Real-time Facial Expression Recognition Based on Boosted Embedded Hidden Markov Model
基于Boost改进的嵌入式隐马尔可夫模型的实时表情识别

Understanding human emotions is one of the necessary skills for the computer to interact intelligently with human users. The most expressive way humans display emotions is through facial expressions. Facial expression recog- nition is necessary for designing any realistic human-machine interfaces. In this paper, we propose a novel framework to real-time facial expression recognition in the interactive computer environment. There are two main contributions of this work. First, we propose a novel network structure and parameters learning algorithm for embedded HMM based on AdaBoost. Second, we apply this optimized embedded HMM to real-time facial expression recognition. In this pa- per, the embedded HMM uses two-dimensional Discrete Cosine Transform (2D-DCT)coefficients as the observation vectors opposite to previous HMM approaches which use pixel intnsities to form the observation vector. The classifi- cation accuracy is improved because our algorithm modifies both the network structure and parameters of embedded HMM. Our proposed system reduces the complexity of the training and recognition system. It can offer 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.