Feature Selection Using Genetic Algorithm for Face Recognition Based on PCA, Wavelet and SVM

Many events, such as terrorist attacks, exposed serious weaknesses in most sophisticated security systems. So it is necessary to improve security data systems based on the body or behavioral characteristics, called biometrics. With the growing interest in the development of human and computer interface and biometric identification, human face recognition has become an active research area. Face recognition offers several advantages over other biometric methods. Nowadays Principal Component Analysis (PCA) has been widely adopted for the face recognition algorithm. Yet still, PCA has limitations such as poor discriminatory power and large computational load. This paper proposed a novel algorithm for face recognition in which a low frequency component of the wavelet is used for PCA representation. Best features of PCA are selected using the genetic algorithm (GA). Support vector machine (SVM) and nearest neighbor classifier ( ND) are used for classification. Classification accuracy is examined by changing number of training images, number of features and kernel function. Results are evaluated on ORL, FERET, Yale and YaleB databases. Experiments showed that proposed method gives a better recognition rate than other popular methods.