Study of Face Recognition Methods

Abstract

Face recognition is an important application of Image processing owing to its use in many fields. Facial recognition and gestures provide intuitional cues for interpersonal communication. Imparting intelligence to computer for identifying facial recognition is a crucial task. Facial recognition are governed by identification of face movement by visual cortex and training a machine to identify these highly in-situ movements is our primary interest. This thesis presents robust facial recognition analysis algorithms for static images
We present an efficient preprocessing method which eliminates the effect of illumination on the detected face images thus making them efficient for feature extraction. Robust Local Binary Patterns and Gabor filters are implemented for feature extraction which are known to provide efficient face representation and analysis. LBP facial features are represented in form of weighted histograms which are best classified using Kullback Leibler divergence measure.
We also present PCA component analysis govern by eigen face recognition and use euclidian distance for matching. We also present sparse based classification which gives better face representation compare to other. The system was tested on YALE Face database B and ORL Face Database.