Based on the analysis of the characteristics of different expressions in the human face

it was found that using the same method to extract the facial features cannot maximize the availability of information. The lost of useful information and the redundant computing may reduce the accuracy and the performance the algorithm. Aiming at the three specific parts on the face-mouth

forehead and eyebrows-which makes the largest changes in shape

texture and distance when facial expression changes take place

a new multi-feature extraction method is developed. To extract the features

it applies template matching on the mouth

edge detection on the forehead and contour detection on the eyebrows. The integration of the three feature extraction outputs becomes the final result of the system. Experiments validated the algorithm with stability

effectiveness

a high recognition accuracy and fast running speed.