Performance Evaluation of Face Recognition Using Gabor Filter, Log Gabor filter and Discrete Wavelet Transform

The face recognition problem is made difficult by the great variability in head rotation and tilt, lighting intensity and angle, facial expression, aging, partial occlusion (e.g. Wearing Hats, scarves, glasses etc.), etc. The Eigenfaces algorithm has long been a mainstay in the field of face recognition and the face space has high dimension. Principal components from the face space are used for face recognition to reduce dimensionality. A multiscale representation for face recognition is done to preserve the discriminant information prior to dimensionality reduction. In this paper, three multiscale representation techniques Gabor filter; Log Gabor filter and Discrete Wavelet Transform are applied prior to dimensionality reduction. PCA is then applied on the above techniques to find the face recognition accuracy rate and to compare the results of the three methods with PCA method. The approximation coefficients in discrete wavelet transform is extracted and it is used to compute the face recognition accuracy instead of using all the coefficients.