Quasi-Frontal Face Recognition Based on Wavelet Decomposition and Support Vector Machines
Liang Tao · Journal of Circuits and Systems · 2003
This paper presents a novel algorithm for quasi-frontal face recognition based on the wavelet decomposition technique and a multi-class Support Vector Machine (SVM) model. The extracted features from human face images by wavelet decomposition are less sensitive to the facial expression variations. The SVM is considered as a good classifier with high generalization performance with no need to add a priori knowledge. The process of the proposed method is as follows: Preprocessing the face images first, then extracting the appropriate features of human faces by wavelet decomposition, training the multi-class SVM model by the face feature vectors, and using the trained SVM model to classify the human faces at last. The ORL database of faces is selected to test and evaluate the proposed algorithm. The results of the test are encouraging and the proposed algorithm is shown to perform very well in recognition capability compared with other approaches for face recognition.