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

Authors:
Ctlin-Daniel Cleanu;Xia Mao;Gilbert Pradel;Sorin Moga;Yuli Xue
Affiliations:
University "POLITEHNICA" Timişoara, Faculty of Electronics and Telecommunications, Av. V. Parvín 2, 300223 Timişoara, Romania;School of Electronic and Information Engineering, Beihang University, Beijing 100083, China;Université d'Evry-Val d'Essonne, Laboratoire Informatique, Biologie Intégrative et Systèmes Complexes, 40 Rue du Pelvoux, 91020 Evry Cedex, France;Institut TELECOM, TELECOM Bretagne, UMR CNRS 3192, Lab-STICC, Technople Brest Iroise, CS 83818, 29238 Brest Cedex 3, France and Université européenne de Bretagne, France;School of Electronic and Information Engineering, Beihang University, Beijing 100083, China
Venue:
Pattern Recognition Letters
Year:
2011

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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.