Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic Visual Learning for Object Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Empirical Performance Analysis of Linear Discriminant Classifiers
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
The Representation and Matching of Pictorial Structures
IEEE Transactions on Computers
Journal of Cognitive Neuroscience
Face recognition system based on PCA and feedforward neural networks
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Face recognition: a convolutional neural-network approach
IEEE Transactions on Neural Networks
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Principal component analysis (PCA) and Linear Discriminant Analysis (LDA) techniques are among the most common feature extraction techniques used for the recognition of faces. In this paper, two face recognition systems, one based on the PCA followed by a feedforward neural network (FFNN) called PCA-NN, and the other based on LDA followed by a FFNN called LDA-NN, are developed. The two systems consist of two phases which are the PCA or LDA preprocessing phase, and the neural network classification phase. The proposed systems show improvement on the recognition rates over the conventional LDA and PCA face recognition systems that use Euclidean Distance based classifier. Additionally, the recognition performance of LDA-NN is higher than the PCA-NN among the proposed systems.