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MMAS Algorithm for Features Selection Using 1D-DWT for Video-Based Face Recognition in the Online Video Contextual Advertisement User-Oriented System

MMAS Algorithm for Features Selection Using 1D-DWT for Video-Based Face Recognition in the Online Video Contextual Advertisement User-Oriented System

Le Nguyen Bao, Dac-Nhuong Le, Gia Nhu Nguyen, Le Van Chung, Nilanjan Dey
Copyright: © 2017 |Volume: 25 |Issue: 4 |Pages: 22
ISSN: 1062-7375|EISSN: 1533-7995|EISBN13: 9781522510802|DOI: 10.4018/JGIM.2017100107
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MLA

Bao, Le Nguyen, et al. "MMAS Algorithm for Features Selection Using 1D-DWT for Video-Based Face Recognition in the Online Video Contextual Advertisement User-Oriented System." JGIM vol.25, no.4 2017: pp.103-124. http://doi.org/10.4018/JGIM.2017100107

APA

Bao, L. N., Le, D., Nguyen, G. N., Van Chung, L., & Dey, N. (2017). MMAS Algorithm for Features Selection Using 1D-DWT for Video-Based Face Recognition in the Online Video Contextual Advertisement User-Oriented System. Journal of Global Information Management (JGIM), 25(4), 103-124. http://doi.org/10.4018/JGIM.2017100107

Chicago

Bao, Le Nguyen, et al. "MMAS Algorithm for Features Selection Using 1D-DWT for Video-Based Face Recognition in the Online Video Contextual Advertisement User-Oriented System," Journal of Global Information Management (JGIM) 25, no.4: 103-124. http://doi.org/10.4018/JGIM.2017100107

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Abstract

Face recognition is an importance step which can affect the performance of the system. In this paper, the authors propose a novel Max-Min Ant System algorithm to optimal feature selection based on Discrete Wavelet Transform feature for Video-based face recognition. The length of the culled feature vector is adopted as heuristic information for ant's pheromone in their algorithm. They selected the optimal feature subset in terms of shortest feature length and the best performance of classifier used k-nearest neighbor classifier. The experiments were analyzed on face recognition show that the authors' algorithm can be easily implemented and without any priori information of features. The evaluated performance of their algorithm is better than previous approaches for feature selection.

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