Automated face recogntion system: Multi-input databases

There has been significant progress in improving the performance of computer-based face recognition algorithms over the last decade. Although algorithms have been tested and compared extensively with each other, there has been remarkably little work comparing the accuracy of computer-based human face recognition systems. We compared eight state-of-the-art face recognition algorithms with three different databases: (i) faces 94; (ii) Olivetti research lab (ORL), and (iii) Indian face database (IFD). The face detection phase had been performed using the morphological features. The recognition results had showed that in linear appearance based classifier; LDA performs better than ICA and PCA in terms of the accuracy of recognition. The computational overhead of LDA and the PCA are almost similar while ICA has a very long execution time. In addition, neural network based on DWT features perform better than classifiers based on other features with 99% recognition rate on the average.