Abstract
This paper presents the results of a comparative study of linear and kernel-based methods for face recognition. We focus mainly on the experimental comparison of classification methods, i.e. Nearest Neighbor, Linear Support Vector Machine, Kernel based Nearest Neighbor and Nonlinear Support Vector Machine. Some interesting conclusions can be obtained after all of these methods are performed on two well-known database, i.e. ORL, YALE Face Database, respectively.
Keywords
- Face Recognition
- Near Neighbor
- Face Database
- Kernel Principal Component Analysis
- Linear Support Vector Machine
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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© 2005 Springer-Verlag Berlin Heidelberg
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Lu, C., Zhang, T., Zhang, W., Yang, G. (2005). An Experimental Evaluation of Linear and Kernel-Based Classifiers for Face Recognition. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3497. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427445_21
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DOI: https://doi.org/10.1007/11427445_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25913-8
Online ISBN: 978-3-540-32067-8
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