Wavelet Based Face Recognition in the Presence of Illumination Variation

As a context of biometrics, significant advances have been made in face recognition during the recent decades. Face recognition is one of the most successful applications of image analysis. The accuracy of automated face recognition is greatly affected by varying in lighting between probe and train images. Difference in lighting condition is one of the difficulties in automated face recognition systems. Histogram equalization technique is widely used to diminish the desired effect of different illumination condition between probe and train images by normalizing variation in illumination. Experiments show that normalizing images that has good lighting condition could lead to an increase in recognition error. Wavelet transform, that is well-known as a multiresolution method, is used in features extracting phase. The multiresolution property of wavelet transform is used in extracting feature leading us to have facial feature descriptors at different scales and frequencies. This thesis presents image quality based technique which is measured in terms of luminance to overcome the disadvantage of varying lighting condition to increase the accuracy of face recognition method. 10-fold cross variation is used to investigate the effect of data selection on classification algorithm. At the end, results are compared to investigate the best method for automated face recognition when illumination variation exists. Keywords: Biometrics, illumination, Wavelets transform (WT), face recognition, feature extraction