An SVM-AdaBoost facial expression recognition system

摘要

This study is focused on improving the recognition rate and processing time of facial recognition systems. First, the skin is detected by pixel based methods to reduce the searching space for maximum rejection classifier (MRC) which detects the face. The detected face is normalized by a discrete cosine transform (DCT) and down-sampled by Bessel transform. Gabor feature extraction techniques were utilized to extract thousands of facial features that represent facial deformation patterns. An AdaBoost-based hypothesis is formulated to select a few hundreds of Gabor features which are potential candidates for expression recognition. The selected features were fed into a saturated vector machine (SVM) classifier to train it. An average recognition rate of 97.57 % and 92.33 % are registered in JAFFE and Yale databases respectively. The execution time of the proposed method is also significantly lower than others. Generally, the proposed method exhibits superior performance than other methods.