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

• Autorzy: Walczak J. , Wojciechowski A.

Identyfikatory

DOI

10.22630/MGV.2016.25.1.3

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

computer vision; gender recognition; DWT; SVM

• Wydawca: Institute of Information Technology of the Warsaw University of Life Sciences – SGGW
• Czasopismo: Machine Graphics and Vision
• Rocznik: 2016
• Tom: Vol. 25, No. 1/4
• Strony: 27--34
• Opis fizyczny: Bibliogr. 16 poz., il., schem., wykr.

Twórcy

autor

Walczak J.

• Institute of Information Technology, Łódź University of Technology, ul. Wólczańska 215, 90-924 Łódź, Poland

• https://orcid.org/0000-0002-5632-9484

autor

Wojciechowski A.

• Institute of Information Technology, Łódź University of Technology, ul. Wólczańska 215, 90-924 Łódź, Poland

• https://orcid.org/0000-0003-3786-7225

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