The Community for Technology Leaders
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.
INDEX TERMS
CITATION

Y. Huang, S. Liu, J. Hu and W. Deng, "Metric-Promoted Siamese Network for Gender Classification," 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017)(FG), Washington, DC, DC, USA, 2017, pp. 961-966.
doi:10.1109/FG.2017.119
88 ms
(Ver 3.3 (11022016))