Combined pattern search optimization of feature extraction and classification parameters in facial recognition.

Abstract : Constantly, the assumption is made that there is an independent contribution of the individual feature extraction and classifier parameters to the recognition performance. In our approach, the problems of feature extraction and classifier design are viewed together as a single matter of estimating the optimal parameters from limited data. We propose, for the problem of facial recognition, a combination between an Interest Operator based feature extraction technique and a k-NN statistical classifier having the parameters determined using a pattern search based optimization technique. This approach enables us to achieve both higher classification accuracy and faster processing time.
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Journal articles
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https://hal.archives-ouvertes.fr/hal-00667663
Contributor : Frédéric Davesne <>
Submitted on : Wednesday, February 8, 2012 - 10:26:11 AM
Last modification on : Monday, October 28, 2019 - 11:00:22 AM

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Cătălin-Daniel Căleanu, Xia Mao, Gilbert Pradel, Sorin Moga, Yuli Xue. Combined pattern search optimization of feature extraction and classification parameters in facial recognition.. Pattern Recognition Letters, Elsevier, 2011, 32 (9), pp.1250--1255. ⟨10.1016/j.patrec.2011.03.019⟩. ⟨hal-00667663⟩

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