Comparison of PCA and 2D-PCA on Indian Faces
Sekhar Rajendran, Amit Kaul, Ravinder Nath, Ajat Shatru Arora, Sushil Chauhan · 2014
Face recognition is an extensively researched topic by researchers from diverse disciplines. Several unsupervised statistical feature extraction methods have been used in face recognition, out of these in this paper a comparison of the PCA(eigenfaces) and 2D-PCA approaches on Indian Faces has been presented. To test and compare their performances a series of experiments were performed on ORL database, Yale face database and then on an in-house dataset which has been collected over a span of 6 months. The performance parameters compared here are recognition rate and speed with varying number of training images. The application of various preprocessing techniques which can be used to improve their performance has also been studied.