Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Face recognition: A study in information fusion using fuzzy integral
Pattern Recognition Letters
Face Processing: Advanced Modeling and Methods
Face Processing: Advanced Modeling and Methods
A method of face recognition based on fuzzy clustering and parallel neural networks
Signal Processing - Special section: Advances in signal processing-assisted cross-layer designs
Using Signal/Residual Information of Eigenfaces for PCA Face Space Dimensionality Characteristics
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
2D and 3D face recognition: A survey
Pattern Recognition Letters
A New Method for Face Recognition Based on Color Information and a Neural Network
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 02
Journal of Cognitive Neuroscience
Components for face recognition
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
A face recognition system based on local feature characterization
ASB'03 Proceedings of the 1st international conference on Advanced Studies in Biometrics
Face recognition by independent component analysis
IEEE Transactions on Neural Networks
Face recognition using kernel direct discriminant analysis algorithms
IEEE Transactions on Neural Networks
Face recognition by applying wavelet subband representation and kernel associative memory
IEEE Transactions on Neural Networks
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Face recognition has recently received significant attention as one of the challenging and promising fields of computer vision and pattern recognition. It plays a significant role in many security and forensic applications such as person authentication in access control systems and person identification in real time video surveillance systems. This paper studies two appearance-based approaches for feature extraction and dimension reduction, namely, Principal Components Analysis (PCA) and Linear Discriminant Analysis (LDA). Numerical experiments were carried out on the ORL face database and many parameters were investigated, this included the effect of changing the number of training images, scaling factor, and the effect of feature vector length on the recognition rate. Classification is performed using the minimum Euclidean distance. The results suggest that the effect of increasing the number of training images has more significance on the recognition rate than changing the image scale. Correlations obtained from numerical experiments on the ORL face database suggest that as the number of training images increases, PCA would yield slightly higher recognition rates.