The Research Of Facial Expression Recognition Technology Based On GABOR Wavelet Transformation

Facial expression recognition gradually becomes a hot topic among artificial emotion, artificial psychology and pattern recognition. The purpose of facial expression recognition is to let the computer have the ability: communicating through facial expressions. If the computer can recognize human facial expression, it is easy to build a natural and harmonious environment for human-computer interaction. Facial expression recognition has the potential market value and wide application prospect. Facial expression recognition is one of contents of affective computing, and it is a challenging topic in psychology, physiology, image processing, machine vision, pattern recognition and other areas.In this paper, based on the theories of digital image processing and facial recognition, this paper integrates the face detection, image preprocessing, feature extraction of facial expression, expression classification and recognition into facial expression recognition system.Firstly, in this paper lots of literatures and books which come from home or abroad has been read during preliminary preparatory stage, on the basis of these references, this paper discuss the background, applied values and research situation about facial expression recognition. Then, this paper introduces the main existing expression recognition algorithms at home and abroad which involve in the mainstream of facial expression recognition.Secondly, the expression images should be preprocessed before expression recognition. Studying face detection technology deeply, based on the probability distribution of skin color in YCbCr color space, this paper apply the face detection algorithm based on the Gaussian model of skin color. We build the Gaussian model of skin color in the YCbCr color space, and use the model to detect face. Another work of image preprocessing is image normalization. This work involves grayscale equalization, rotation transformation, sacling transformation and image grayscale.Thirdly, on the basis of the advantages of Gabor wavelet and flexible template matching method on the facial expression recognition, we explore the expression recognition method based on Gabor wavelet and flexible template matching. At the same time, based on face detection, a family of Gabor filters which involves multi-scale and multi-orientation is used to extract the features of expression. And then Euclidean distance and KNN strategy are used to recognize and classify the facial expression. Experimental results demonstrate the feasibility of the method.Finally, this paper adopt the algorithms which are described above to design and develop a facial expression recognition system. The development tool this paper use is Visual Studio C++ 6.0 development tool in the Windows XP platform. According to the experimental results on two expression databases, the performance of facial expression recognition system has basically reached the expected requirements.