Face Recognition using Feed Forward Neural Network

In this paper, we propose four techniques for extraction of facial features namely 2DPCA, LDA, KPCA and KFA. The purpose of face feature extraction is to capture certain discriminative features that are unique for a person. In the previous works that uses PCA for face feature extraction involves merging the features and reducing the dimensions that results in some information loss. To overcome this problem, in the first method we used 2D-PCA for face feature extraction, LDA for making class labels and FFNN for face recognition. Similarly we used the three more techniques namely LDA, KPCA and KFA for face feature extraction and then FFNN for face recognition.The proposed method is tested on the ORL database. Results involve the comparison of face recognition rate of the different feature extraction techniques with FFNN using 10, 20, 30 and 40 hidden neurons. The results on ORL database shows that FFNN with 30 hidden neurons performs best by extracting features using KPCA.