Face Recognition Framework Based on Correlated Images and Back-Propagation Neural Network

The human face facial complexity and the face changes make the face recognition system a challenging task to design and difficult to implement. The correlation between the training images which has a high impact on the accuracy of the face recognition system never considered by researchers. In this paper, we presented an enhanced framework to improve the face recognition using the classical conventional Principal Component Analysis (PCA) and the Back-Propagation Neural Network (BPNN). A key contribution of this work is based on obtaining a robust training dataset called the T-Set using the correlation between all the images in the training dataset not based on the image density which adds a distinct layer between the dataset. We used the PCA descriptor for features extraction and dimension reduction to show that there is a promising enhancement even with using traditional algorithms. We combined five distance methods (Correlation, Euclidean, Canberra, Manhattan, and Mahalanobis) to obtain the T-Set using the square-root of the sum of the squares to achieve higher accuracy. We added a strength factor to each of the distance methods, and we achieved higher face recognition accuracy than the current approach. Our experimental results on YALE and ORL datasets demonstrate that the approach we proposed improved the accuracy of face recognition system with respect to the existing methods.