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Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 36, 2019 No. 3
940-944

Face gender recognition based on multi-layer feature fusion convolution neural network with adjustable supervisory function

Shi Xuechao
Zhou Yatong
Chi Yue
Tianjin Key Laboratory of Electronic Materials & Devices, School of Electronics & Information Engineering, Hebei University of Technology, Tianjin 300401, China
DOI: 10.19734/j.issn.1001-3695.2017.10.0976

Abstract

In order to further improve the accuracy of gender recognition, this paper proposed the convolution neural network model based on multi-layer feature fusion with adjustable supervisory function, L-MFCNN, then used it for face gender recognition. Unlike the traditional convolution neural network, L-MFCNN combined the output of multiple shallow convolution layers with the final convolution layer output. Fusion the characteristics of multi-layer convolutions, not only used the high-level semantic information, but also considered the bottom of the details of the texture information, making the face gender recognition more accuracy. While using the large-margin softmax loss could adjust the margin function, it could explicitly encourages the same gender intra-class compactness and the different gender inter-class separability to get better face gender recognition. The face gender recognition experiment data on multiple face data sets show that the accuracy of L-MFCNN is higher than that of traditional convolution network. Besides, L-MFCNN also provides the new ideas and directions for the future gender recognition of face.

Foundation Support

国家自然科学基金资助项目(61401307)
河北省科学技术研究与发展项目(11213565)
河北省引进留学人员资助项目(CL201707)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2017.10.0976
Publish at: Application Research of Computers Printed Article, Vol. 36, 2019 No. 3
Section: Technology of Graphic & Image
Pages: 940-944
Serial Number: 1001-3695(2019)03-060-0940-05

Publish History

[2019-03-05] Printed Article

Cite This Article

石学超, 周亚同, 池越. 基于多层特征融合可调监督函数卷积神经网络的人脸性别识别 [J]. 计算机应用研究, 2019, 36 (3): 940-944. (Shi Xuechao, Zhou Yatong, Chi Yue. Face gender recognition based on multi-layer feature fusion convolution neural network with adjustable supervisory function [J]. Application Research of Computers, 2019, 36 (3): 940-944. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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