An Illumination Invariant Robust and Fast Face Detection, Feature Extraction Based Face Recognition System

Abstract

This paper proposes a fast and efficient approach for face recognition under non uniform illumination variations. Robust Haar classifiers technique is used for face detection from an image. Since illumination variations lie in low frequency DCT coefficients, illumination variations is removed from detected face by rescaling down an appropriate number of low frequency DCT coefficients while still preserving important facial features. Further, since, important facial features are concentrated in small number of DCT oefficients, face feature vector is generated by discarding high frequency coefficients. K-means clustering is employed to reduce search space complexity. Face recognition is performed by comparing feature vector of test image with feature vector of images in the closest matching cluster using Euclidean distance. Experimental results on Yale database, Caltech database, IMM database and Extended Yale face database B show that the proposed approach improves face recognition rate upto 100% along with significantly reduced search space complexity and low computational cost. Equal error rate (EER) is acquired by plotting false acceptance rate (FAR) and false reject rate (FRR) against different threshold values.