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2018 | OriginalPaper | Chapter

Gender Classification Based on Facial Shape and Texture Features

Authors : Mayibongwe H. Bayana, Serestina Viriri, Raphael Angulu

Published in: Advances in Visual Computing

Publisher: Springer International Publishing

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Abstract

This paper seeks to improve gender classification accuracy by fusing shape features, the Active Shape Model with the two appearance based methods, the Local Binary Pattern (LBP) and Local Directional Pattern (LDP). A gender classification model based on the fusion of appearance and shape features is proposed. The experimental results show that the fusion of the LBP and LDP with the Active Shape Model improved the gender classification accuracy rate to 94.5% from 92.8% before fusion.

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Title: Gender Classification Based on Facial Shape and Texture Features
Authors: Mayibongwe H. Bayana
Serestina Viriri
Raphael Angulu
Publisher: Springer International Publishing
Book: Advances in Visual Computing
Print ISBN: 978-3-030-03800-7

Electronic ISBN: 978-3-030-03801-4

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-030-03801-4_15

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