Facial Affective Recognition Based On Gabor Wavelet Transformation And Fractal Dimension

Affective computing is more and more noticed by many researchers, because computer has an advantage in image area. Affective computing is a multidisciplinary synthetic area, which tries to enable computer to have the ability of understanding and expressing affection, just like human beings. And it plays an important role in intelligent human computer interface. Facial affective recognition is very valuable in the interrelated areas of artificial intelligence, computer vision, image management, pattern recognition, and psychology and so on.Facial affective recognition is an active research area in pattern recognition. In broad sense, facial affective recognition of static images includes pretreatment, face detection, affective feature extraction and affective classification. In this paper, because each image only has a face and it is gray, we apply JAFFE database to making research mainly in the following aspects:(1) Firstly, we segment each face from initial images and get rid of yawp. Then make a standardization management so as to attract effective features, which include size standardization and gray standardization. Size standardization is that all images are transformed to the standard size and the marked corresponding key feature points are also adjusted to fixed position. Gray standardization is that the gray of all images proceeded above is transformed to the same gray level. Finally, we plot out each image further by fixed pixels.(2)We present a particular feature extraction algorithm—Gabor wavelet transformation combines with fractal dimension. For each segmented grid, we make Gabor wavelet transformation and fractal dimension compute. To improve efficiency and low the dimension, we regard the average and variance of the module from Gabor wavelet transformation along with the results from fractal dimension compute as affective feature vector of each grid.(3)We solve the multi-classification task by using back propagation neural networks and radial basis function neural networks in Matlab, because Matlab is convenient in applying different kernel functions and incentive functions.By using these methods of pretreatment, feature extraction algorithm and classification algorithm, the experiment can realize a fast and robust recognition task. The experiment results have testified the efficiency.