Abstract
2D face classification problem is very difficult because of the illumination, pose, expression and occlusion factor. The sparse Representation based classification (SRC) provides a significant amount of robustness against to illumination changes and noise. In this study, sparse approximation is used on face recognition and applied to “Extended Yale Face B” database. Results of sparse approximation classification are compared with Fisher Linear Discriminator.
Keywords
Sparse approximation
Facial recognition system
Robustness (evolution)
Artificial intelligence
Pattern recognition (psychology)
Discriminator
Computer science
Face (sociological concept)
Contextual image classification
Image (mathematics)
Subject Areas
Face and Expression Recognition
·Computer Vision and Pattern Recognition ·Physical Sciences
Face recognition and analysis
·Computer Vision and Pattern Recognition ·Physical Sciences
Biometric Identification and Security
·Signal Processing ·Physical Sciences
Citations by Year
2017: 1
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