Human Face Recognition using PCA Eigenfaces

Abstract

This work aims to present face recognition solution using eigenfaces which can be used for various applications like the online attendance system, access control and others. The patterns in human faces have been extracted using the Principal Component Analysis (PCA) and the extracted eigenfaces, which are the eigenvectors of the covariance matrix, represent the extracted features. Euclidean distance based classifier has been utilized which compares the Euclidean distance between the test image and the face images in the training set to ascertain the class to which the test image should belong. Two varying datasets are used in this work with first containing selected facial images from that AT&T or the Olivetti Research Laboratory facial database while second set has been generated locally at DHA Suffa University, Karachi. Seventy percent of the images have been used for training while the remaining thirty percent images have been used for evaluating the classifier. The implemented algorithm is developed in MATLAB and ensures an overall efficiency of around ninety percent for slight variations in facial expressions and postures.