Comparison of Different Neural Network Architectures for Classification of Feature Transformed Data for Face Recognition

Abstract

In this paper neural network classifier is applied on transformed shape features for face recognition. Classification by neural networks to a large extent depends on the neural network architecture. We have investigated three different neural network architectures for classification namely-Feed Forward Neural Network, Cascade Feed Forward Neural Network and Radial Basis Function Neural Network and tested their performance for three sets of feature extracted data. For feature extraction we convert the 2-D gray level face images into their respective depth maps or physical shape which are subsequently transformed by three different methods to get three separate data sets,namely-Coiflet Packet , Radon Transform and Fourier Mellin Transform to compute energy for feature extraction. After feature extraction each of the training classes are optimally separated using linear discriminant analysis. The neural network classifiers have been tested on each of the three sets of feature extracted data and a comparative analysis has been done on the results obtained. The proposed algorithms have been tested on the ORL database, widely used for face recognition experiments. General Terms Pattern Recognition, Computer Vision, Artificial Intellegence,Image Processing