American Journal of Networks and Communications 2015; 4(4): 90-94 Published online July 7, 2015 (http://www.sciencepublishinggroup.com/j/ajnc) doi: 10.11648/j.ajnc.20150404.12 ISSN: 2326-893X (Print); ISSN: 2326-8964 (Online) A Novel Hybrid Method for Face Recognition Based on 2d Wavelet and Singular Value Decomposition Vahid Haji Hashemi 1, * , Abdorreza Alavi Gharahbagh 2 1 Computer Engineering, Faculty of Engineering, Kharazmi University of Tehran,Tehran, Iran 2 Department of Electrical and Computer Engineering, Islamic Azad University, Shahrood, Iran Email address: hajihashemi.vahid@yahoo.com (V. H. Hashemi), R_alavi@iau-shahrood.ac.ir (A. A. Gharahbagh) To cite this article: Vahid Haji Hashemi, Abdorreza Alavi Gharahbagh. A Novel Hybrid Method for Face Recognition Based on 2d Wavelet and Singular Value Decomposition. American Journal of Networks and Communications. Vol. 4, No. 4, 2015, pp. 90-94. doi: 10.11648/j.ajnc.20150404.12 Abstract: An efficient face recognition system using eigen values of wavelet transform as feature vectors and radial basis function (RBF) neural network as classifier is presented. The face images are decomposed by 2-level two-dimensional (2-D) wavelet transformation. The wavelet coefficients obtained from the wavelet transformation are averaged for finding centers of features. In train process, four output of wavelet transform is analyzed and all eigenvalues of these images is obtained. At next step, the maximum 10 eigenvalues of wavelet sub images is stored as feature. Based on four sub images of wavelet transform and 10 eigenvalues of each sub image, the length of feature vector is 40. After obtaining features, in the train process for each person a center that has minimum Euclidean distance from all features is selected using RBF function. In fact the features are recognized by a RBF network. For a new input face image, firstly the feature vector is computed and then the distance (error) of this new vector with all centers of all persons is checked. The minimum distance is selected as target face. The proposed method on Essex face database and results showed that the proposed method provide better recognition rates with low computational complexity. Keywords: Face Recognition, Singular Value Decomposition, SVD, Wavelet, Radial Basis Function, Neural Network 1. Introduction Recognition of human faces is a very important task in many applications such as robotics, artificial intelligence, security systems etc. The wide category of face images such as its scene, brightness, lights, etc, are challenges for face detection algorithms. A face recognition system must be reliable and robust about all variable conditions of face images such as viewpoint, illumination, rotation, etc. The main tasks in the face recognition system are training with minimum face images and classification a new image based on its train process. Many researchers work about face recognition [1].In [1] face recognition methods classify to some groups. In the first group that named appearance based, total of face images or face objects are analyzed directly. Turk and Pent in [2] suggest using the eigenvalues in face recognition that is also popular than apparent base method. In [2] face matrix eigen values and vectors is computed and called Eigen faces, which are the principal components of face images. LDA 1 could be analyzed directly face images to extract the Fisher face [3] or analyzed the Eigen face to obtain a criteria for Eigen features of each face [4].Jain Yang et al. have developed a new technique by two-dimensional principal component analysis 2 for image representation. 2DPCA is based on 2D image matrices so the image matrix does not need to be transformed into a vector [5]. Rajagopalan proposeda system using multiple facial features extracted from the face [6].Image representation is a popular method in many image processing algorithms. Wavelet and FFT based feature extraction methods has many advantages. An appropriate wavelet transform is so robust regard to lighting changes and be capable of capturing substantial spatial features while has low computational complexity low. Bai-Ling Zhang use WaveletTransform and choosethe lowest resolution subband coefficients as face features [7]. Another technique is WPD 3 that build a compact and meaningful feature and is also used in face recognition method [8]. WPD 1 Linear Discriminant Analysis 2 2DPCA 3 Wavelet Packet Decomposition