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3D Multi-Spectrum Sensor System with Face Recognition
Sensors 2013, 13(10), 12830-12851; doi:10.3390/s131012830

Best Basis Selection Method Using Learning Weights for Face Recognition

2,*  and 1
1 The School of Electrical and Electronic Engineering, Yonsei University, 134 Shinchon-Dong, Seodaemun-Gu, Seoul 120-749, Korea 2 The School of Electrical Electronic and Control Engineering, Kongju National University, 275 Budae-Dong, Seobuk-Gu, Cheonan, Chungnam 331-717, Korea
* Author to whom correspondence should be addressed.
Received: 24 July 2013 / Revised: 26 August 2013 / Accepted: 16 September 2013 / Published: 25 September 2013
(This article belongs to the Section Physical Sensors)
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In the face recognition field, principal component analysis is essential to the reduction of the image dimension. In spite of frequent use of this analysis, it is commonly believed that the basis faces with large eigenvalues are chosen as the best subset in the nearest neighbor classifiers. We propose an alternative that can predict the classification error during the training steps and find the useful basis faces for the similarity metrics of the classical pattern algorithms. In addition, we also show the need for the eye-aligned dataset to have the pure face. The experiments using face images verify that our method reduces the negative effect on the misaligned face images and decreases the weights of the useful basis faces in order to improve the classification accuracy.
Keywords: feature selection; similarity metrics; learning weights feature selection; similarity metrics; learning weights
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Lee, W.; Cheon, M.; Hyun, C.-H.; Park, M. Best Basis Selection Method Using Learning Weights for Face Recognition. Sensors 2013, 13, 12830-12851.

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