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2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017) (2017)
Washington, DC, DC, USA
May 30, 2017 to June 3, 2017
ISBN: 978-1-5090-4023-0
pp: 961-966
ABSTRACT
Gender classification is a fundamental and important application in computer vision, and it has become a research hotspot. Real-world applications require gender classification in unconstrained conditions where traditional methods are not appropriate. This paper proposes a Deep Convolutional Neural Network for feature extraction together with fully-connected layers for metric learning. A Siamese network is built for similarity measuring to promote the performance of classification. Extensive experiments on several databases demonstrate that a significant improvement can be obtained for gender classification tasks in both constrained and unconstrained conditions.
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CITATION
Yipeng Huang, Shuying Liu, Jiani Hu, Weihong Deng, "Metric-Promoted Siamese Network for Gender Classification", 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017), vol. 00, no. , pp. 961-966, 2017, doi:10.1109/FG.2017.119
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