LGCN: Learnable Gabor Convolution Network for Human Gender Recognition in the Wild
Author:
Affiliation:
1. Institute of Semiconductors, CAS
2. Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences
Publisher
Institute of Electronics, Information and Communications Engineers (IEICE)
Subject
Artificial Intelligence,Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Hardware and Architecture,Software
Link
https://www.jstage.jst.go.jp/article/transinf/E102.D/10/E102.D_2018EDL8239/_pdf
Reference17 articles.
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3. [3] G. Levi and T. Hassncer, “Age and gender classification using convolutional neural networks,” 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp.34-42, 2015. 10.1109/cvprw.2015.7301352
4. [4] M. Duan, K. Li, C. Yang, and K. Li, “A hybrid deep learning CNN-ELM for age and gender classification,” Neurocomputing, vol.275, pp.448-461, 2017. 10.1016/j.neucom.2017.08.062
5. [5] S.S. Sarwar, P. Panda, and K. Roy, “Gabor filter assisted energy efficient fast learning convolutional neural networks,” 2017 IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED), pp.1-6, 2017. 10.1109/islped.2017.8009202
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