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Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 36, 2019 No. 2
593-597,605

Face detection algorithm based on scale-independent cascade convolution neural network

Zheng Chenghaoa
Liu Binga,b
Zhou Yonga
a. College of Computer Science & Technology, b. Institute of Electrics, Chinese Academy of Sciences, China University of Mining & Technology, Xuzhou Jiangsu 221116, China
DOI: 10.19734/j.issn.1001-3695.2017.08.0957

Abstract

Since the convolution neural network needs to input a fixed size image when performing image processing, this will lead to the loss of most of the original information in the scaling process. In addition, the feature extraction of images will not be put in place when the network has only one structure. To solve the above two problems, this paper presented a face detection algorithm combining cascade convolution neural network and spatial pyramid pooling. In this method, it cascaded three different convolution neural network models, the structure of which were from simple to complex, and extracted different face features at different levels to complete the detection of the face areas of images. At the same time, it added the pyramid pool at each level of the network, and this pooling strategy did not require a fixed size input, increasing dimension selection of model input. Compared with the traditional method, this method realizes the multi-scale input of the model, improves the detection performance, and reduces the time of face detection in the standard face data set.

Foundation Support

国家自然科学基金青年科学基金资助项目(61403394)
国家自然科学基金面上项目(61572505)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2017.08.0957
Publish at: Application Research of Computers Printed Article, Vol. 36, 2019 No. 2
Section: Technology of Graphic & Image
Pages: 593-597,605
Serial Number: 1001-3695(2019)02-060-0593-05

Publish History

[2019-02-05] Printed Article

Cite This Article

郑成浩, 刘兵, 周勇. 尺度无关的级联卷积神经网络人脸检测算法 [J]. 计算机应用研究, 2019, 36 (2): 593-597,605. (Zheng Chenghao, Liu Bing, Zhou Yong. Face detection algorithm based on scale-independent cascade convolution neural network [J]. Application Research of Computers, 2019, 36 (2): 593-597,605. )

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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