An Efficient Technique for Facial Expression Recognition Using Multistage Hidden Markov Model

Partition-based feature extraction is widely used in the pattern recognition and computer vision. This method is robust to some changes like occlusion, background, etc. In this paper, partition-based technique is used for feature extraction and extension of HMM is used as a classifier. The new introduced multistage HMM consists of two layers. In which bottom layer represents the atomic expression made by eyes, nose, and lips. Further upper layer represents the combination of these atomic expressions such as smile, fear, etc. Six basic facial expressions are recognized, i.e., anger, disgust, fear, joy, sadness, and surprise. Experimental result shows that proposed system performs better than normal HMM and has the overall accuracy of 85% using JAFFE database.