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Improving Gender Classification Using an Extended Set of Local Binary Patterns

Improving Gender Classification Using an Extended Set of Local Binary Patterns

Abbas Roayaei Ardakany, Mircea Nicolescu, Monica Nicolescu
Copyright: © 2014 |Volume: 5 |Issue: 3 |Pages: 20
ISSN: 1947-8534|EISSN: 1947-8542|EISBN13: 9781466655638|DOI: 10.4018/ijmdem.2014070103
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MLA

Ardakany, Abbas Roayaei, et al. "Improving Gender Classification Using an Extended Set of Local Binary Patterns." IJMDEM vol.5, no.3 2014: pp.47-66. http://doi.org/10.4018/ijmdem.2014070103

APA

Ardakany, A. R., Nicolescu, M., & Nicolescu, M. (2014). Improving Gender Classification Using an Extended Set of Local Binary Patterns. International Journal of Multimedia Data Engineering and Management (IJMDEM), 5(3), 47-66. http://doi.org/10.4018/ijmdem.2014070103

Chicago

Ardakany, Abbas Roayaei, Mircea Nicolescu, and Monica Nicolescu. "Improving Gender Classification Using an Extended Set of Local Binary Patterns," International Journal of Multimedia Data Engineering and Management (IJMDEM) 5, no.3: 47-66. http://doi.org/10.4018/ijmdem.2014070103

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Abstract

In this article, the authors designed and implemented an efficient gender recognition system with high classification accuracy. In this regard, they proposed a novel local binary descriptor capable of extracting more informative and discriminative local features for the purpose of gender classification. Traditional Local binary patterns include information about the relationship between a central pixel value and those of its neighboring pixels in a very compact manner. In the proposed method the authors incorporate into the descriptor more information from the neighborhood by using extra patterns. They have evaluated their approach on the standard FERET and CAS-PEAL databases and the experiments show that the proposed approach offers superior results compared to techniques using state-of-the-art descriptors such as LBP, LDP and HoG. The results demonstrate the effectiveness and robustness of the proposed system with 98.33% classification accuracy.

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