Face Recognition Using Principal Component Analysis with Euclidean Distance and Artificial Neural Network Classification Methods

Abstract

Principal component analysis is a multivariate statistical method used for reducing data dimension which can be applied in various fields. One of them is in computer science application, i.e. dimension reduction of image data for face recognition. This research focuses on obtaining average faces matrix, eigenfaces, and data projection results based on principal component scores. The results were then used for further step in face recognition namely classification. Here two classification methods are used, namely Euclidean distance and artificial neural networks (ANN) with single and multi-layers. The data used are from AT&T Laboratories Cambridge collections in April 1992 - April 1994, each line of the data contains pixel from a single image that has 256 levels of black and white color between 0 and 1. Based on the analysis results, the accuracy rate of face recognition using the Euclidean distance method is 89%. At the same time, single-layer ANN produce the accuracy of 90.5% for two hidden layers, 89% for 3 and 4 hidden neurons, while multi-layer ANN produce the accuracy of 89.5% for hidden neurons (3.2), and 91% for hidden neurons (3,3) and (4,2). However, the smallest error was obtained by multi-layer ANN with hidden neurons (4,2) which resulted the error value of 7.5303. Thus we conclude that the multilayer ANN (4.2) outperformed the others and then is chosen as the best classification for the face recognition analysis of the data.