Improved gender classification using Discrete Wavelet Transform and hybrid Support Vector Machine

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Published:
Dec 20, 2016
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Vol. 25 No. 1/4 (2016)
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Original research papers
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Jakub Walczak
Institute of Information Technology, Łódź University of Technology
https://orcid.org/0000-0002-5632-9484
Adam Wojciechowski
Institute of Information Technology, Łódź University of Technology
https://orcid.org/0000-0003-3786-7225

DOI: https://doi.org/10.22630/MGV.2016.25.1.3
Keywords : computer vision, gender recognition, DWT, SVM
Abstract
Gender recognition, across different races and regardless of age, is becoming an increasingly important technology in the domains of marketing, human-computer interaction and security. Most state-of-the-art systems consider either highly constrained conditions or relatively large databases. In either case, often not enough attention is paid to cross-racial age-invariant applications. This paper proposes a~method of hybrid classification, which performs well even with a small training set. The design of the classifier enables the construction of reliable decision boundaries insensitive to an aging model as well as to race variation. For a training set consisting of one hundred images, the proposed method reached an accuracy level of 90%, whereas the best method known from the literature, tested under the restrictions imposed on the database, achieved only 78% accuracy.

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How to Cite
Walczak, J., & Wojciechowski, A. (2016). Improved gender classification using Discrete Wavelet Transform and hybrid Support Vector Machine. Machine Graphics and Vision, 25(1/4), 27–34. https://doi.org/10.22630/MGV.2016.25.1.3
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