Face Recognition Using Wavelets Transform and 2 D PCA by SVM Classifier

Among the various biometric methods, face recognition has become one of the most challenging tasks in the pattern recognition field during the past decades. An integrated algorithm for face recognition is proposed based on the respective advantages of wavelets transform (WT), 2D Principle Component Analysis (PCA) and Support Vector Machines (SVM) in this paper. At first stage, the original images are decomposed into low frequency images by applying wavelets transform, the high-frequency components were ignored, while the low-frequency components which contains the primary information can be obtained. And then 2D PCA algorithm is used to deal with feature extraction. After generating feature vector, distance classifier and SVM are used for classification stage. Experiments with two face databases show that the proposed method has accuracy and robust for face recognition.