Improving the Recognition of Faces using PCA and SVM Optimized by DWT

Abstract

Automatic recognition of people has received much attention during the recent years due to its many applications in different fields such as law enforcement, security applications or video indexing.In this paper,Wavelet transform is used for preprocessing the images in order to handle bad illumination, Principle Component Analysis (PCA) is used to play a key role in feature extractor and the SVM were used for classification.Support Vector Machines (SVMs) have been recently proposed as a new classifier for pattern recognition. We illustrate the potential of SVMs on the Cambridge ORL Face database, which consists of 400 images of 40 individuals, containing quite a high degree of variability in expression, pose, and facial details. The SVMs that have been used included the Linear (LSVM), Polynomial (PSVM), and Radial Basis Function (RBFSVM) SVMs, we obtain recognition rates as high as 97,9 in ORL face database with polynomial kernel (PSVM).