Many recent events, such as terrorist attacks, exposed defects in most sophisticated security systems. Therefore, it is necessary to improve security data systems based on the body or behavioral characteristics, often called biometrics. Together 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 appears to offer 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 proposes a novel algorithm for face recognition using a mid band frequency component of partial information which is used for PCA representation. Because the human face has even symmetry, half of a face is sufficient for face recognition. This partial information saves storage and computation time. In comparison with the traditional use of PCA, the proposed method gives better recognition accuracy and discriminatory power. Furthermore, the proposed method reduces the computational load and storage significantly

Abstract: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.

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Abstract-Face recognition has advantages over other biometric methods. Principal Component Analysis (PCA) has been widely used for the face recognition algorithm. PCA has limitations such as poor discriminatory power and large computational load. Due to these limitations of the existing PCA based approach, we used a method of applying PCA on wavelet subband of the face image and two methods are proposed to select best of the eigenvectors for recognition. The proposed methods select important eigenvectors using genetic algorithm and entropy of eigenvectors. Results show that compared to traditional method of selecting top eigenvectors, proposed method gives better results with less number of eigenvectors.

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