Face Recognition Using PCA, FLDA and Artificial Neural Networks

Face recognition is a system that identifies human faces through complex computational techniques. The paper explains two different algorithms for feature extraction. These are Principal Component Analysis and Fisher Faces algorithm. It then explains how images can be recognized using a backpropagation algorithm on a feed forward neural network. Two training databases one containing 20 images and the other containing 80 images are used to test the proposed techniques. Later the results are compared and tabulated. Keywords— eigenfaces, fisherfaces, neural network, backpropagation, minimum euclidean distance