The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
Kernel Eigenfaces vs. Kernel Fisherfaces: Face Recognition Using Kernel Methods
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Enhanced (PC)2 A for face recognition with one training image per person
Pattern Recognition Letters
Acquiring Linear Subspaces for Face Recognition under Variable Lighting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
Semi-random subspace method for face recognition
Image and Vision Computing
Feature Extraction & Image Processing, Second Edition
Feature Extraction & Image Processing, Second Edition
Comparing and combining lighting insensitive approaches for face recognition
Computer Vision and Image Understanding
Proceedings of the CUBE International Information Technology Conference
Proceedings of the CUBE International Information Technology Conference
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This paper proposes a spectrum-based approach for enhancing the performance of a Face Recognition (FR) system, employing the unique combination of Discrete Fourier Transform (DFT), Discrete Cosine Transform (DCT) and Binary Particle Swarm Optimization (BPSO). Individual stages of the FR system are examined and an attempt is made to improve each stage. DFT and DCT are used for efficient feature extraction and BPSO-based feature selection algorithm is used to search the feature space for the optimal feature subset. Experimental results, obtained by applying the proposed algorithm on Cambridge ORL, Extended Yale B and Color FERET face databases, show that the proposed system outperforms other FR systems. A significant increase in the recognition rate and a substantial reduction in the number of features is observed. Dimensionality reduction obtained is around 96% for ORL and more than 99% for Extended Yale B and Color FERET databases.