www.ijsret.org 852 International Journal of Scientific Research Engineering & Technology (IJSRET), ISSN 2278 – 0882 Volume 3, Issue 5, August 2014 Comparative Analysis of Face Recognition using DCT, DWT and PCA for Rotated faces Debaraj Rana 1 , Sunita Dalai 2 , Bhawna 3 , Sujata Minz 4 , N. Prasanna 5 , Tapasri Tapasmita Sahu 6 1,2 Department of ECE, Asst. Professor, Centurion University of Technology & Management, Odisha, INDIA 3, 4, 5, 6 Department of ECE, B.Tech Students, Centurion University of Technology & Management, Odisha, INDIA ABSTRACT Face recognition has been an active research area in the pattern recognition and computer vision domains. Human’s day to day actions are increasingly being handled electronically, instead of face to face. Face is a complex multidimensional structure and needs good computing techniques for recognition. The main aim of Face Recognition system is to retrieve face images which are similar to a specific query face image in large face Databases. The retrieved face images can be used for many applications. In this paper we have done face recognition using Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) and Principal Component Analysis (PCA). to varieties of test images which are rotated 15° towards right, 15° towards left, 30° towards right, 30° towards left, with low illumination and different facial expressions. Then we have developed a comparative analysis between all the three techniques based on recognition rate. Keywords - DCT, DWT, Face Recognition, PCA I. INTRODUCTION Face recognition (FR) has emerged as one of the most extensively studied research topics that spans multiple disciplines such as pattern recognition, signal processing and computer vision [6]. This is due to its numerous important applications in identity authentication, security access control, intelligent human-computer interaction, and automatic indexing of image and video databases. Face recognition has repeatedly shown its importance over the last ten years or so. Not only is it a vividly researched area of image analysis, pattern recognition and more precisely biometrics but also it has become an important part of our everyday lives since it was introduced as one of the identification methods to be used in e-passports and in many security purposes. The human face is full of information but working with all the information is time consuming and less efficient. It is better get unique and important information and discards other useless information in order to make system efficient. Face recognition [6] systems can be widely used in areas where more security is needed. Researchers have developed varieties of new techniques to improve the face recognition rate. Also different people gone for different approaches like Geometric /Template Based Approaches, Piecemeal/ Holistic Approaches, Template/Statistical/ Neural Network Approaches [22]. In this paper we have done the face recognition using statistical and frequency domain approach. We have applied DCT, DWT and PCA for face recognition. Face recognition techniques applied to FEI database from which we have taken 180 test images and these test images are of different varieties like frontal face with different expression, low illumination also some are rotated 15 O , 30 O towards right as well as left. After applying all the three technique to the test images then we have developed a comparative analysis among the three techniques based on the recognition rate. II. DISCRETE COSINE TRANSFORM (DCT) DCT has emerged as a popular transformation technique widely used in signal and image processing. This is due to its strong “energy compaction” property: most of the signal information tends to be concentrated in a few low-frequency components of the DCT [2, 16]. DCT transforms the input into a linear combination of weighted basis functions. These basis functions are the frequency components of the input data. DCT is similar to the discrete Fourier transform (DFT) in the sense that they transform a signal or image from the spatial domain to the frequency domain, use sinusoidal base functions, and exhibit good de-correlation and energy compaction characteristics. The major difference is that the DCT transform uses simple cosine-based basis functions whereas the DFT is a complex transform and therefore stipulates that both image magnitude and phase