Feature Selection Methods for Boosted Crosspectral Face Recognition
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Summary
Three novel feature selection methods are proposed: Genuine segment score thresholding, d′-based thresholding and two Adaboost inspired methods to prune irrelevant information in encoded data and to improve performance of the Boosted LGPI technique.
- Type
- dissertation
- Published
- 2011-08-01
- Cited by
- 0
- References
- 45
- Access
- Open access
- OpenAlex
- https://openalex.org/W39208100
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:59650448
Keywords
Artificial intelligence, Thresholding, Pattern recognition (psychology), Computer science, Facial recognition system
References
- Introduction to the special issue on multimedia implementation », IEEE Trans. On Circuits and Systems for Video Technology
- Biometrics, Personal Identification in Networked Society: Personal Identification in Networked Society
- The Feature Selection Problem: Traditional Methods and a New Algorithm
- Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning
- Correlation-based Feature Selection for Machine Learning
- On Feature Selection: Learning with Exponentially Many Irrelevant Features as Training Examples
- On Information and Sufficiency
- Feature selection for high-dimensional genomic microarray data
- Selection of Relevant Features in Machine Learning
- Handbook of Fingerprint Recognition
- Introduction to statistical pattern recognition (2nd ed.)
- Heterogeneous Face Recognition: Matching NIR to Visible Light Images
- IR and visible light face recognition
- A decision-theoretic generalization of on-line learning and an application to boosting
- Face recognition: A literature survey
- Symmetry, probability, and recognition in face space
- Multiscale Fusion of Visible and Thermal IR Images for Illumination-Invariant Face Recognition
- Wrappers for Feature Subset Selection
- Learning a kernel matrix for nonlinear dimensionality reduction
- Nonlinear dimensionality reduction by locally linear embedding.
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