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Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 37, 2020 No. 12
3777-3780,3835

Real-time micro-expression recognition algorithm based on atrous convolutions for CNN

Lai Zhenyi
Chen Renhe
Qian Yurong
School of Software, Xinjiang University, Urumqi 830000, China
DOI: 10.19734/j.issn.1001-3695.2019.07.0273

Abstract

Based on the depth feature of facial micro-expression recognition, such as CNN, the classification method of facial micro-expression recognition is gradually improved. Compared with the traditional feature extraction method, it is easier to meet the real-time application. In order to perfect the details and extract the fine features of micro-expressions, this paper proposed a new algorithm combining the atrous convolutions kernel and the automatic correction of face to improve the feature extraction process of CNN network. It trained and tested the model on CASME and CASME Ⅱ micro-expression public data sets through real-time recognition in real-time application of automatic face correction. It improved the robustness of the model by comparing the loss function schemes. The accuracy of the method in CASME is 70.16% and witch in CASMEⅡ is 72.26%. Real-time recognition frame rate at 60 fps. This method can effectively improve the accuracy of micro-expression recognition, meet the real-time requirements, and has good robustness and generalization ability.

Foundation Support

国家自然科学基金资助项目(61562086,61966035)
新疆维吾尔自治区教育厅创新团队项目(XJEDU2016S035)
新疆维吾尔自治区自然科学基金资助项目(2018D01C036)
新疆维吾尔自治区研究生创新项目(XJ2019G071,XJ2019G069,XJ2019G072)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2019.07.0273
Publish at: Application Research of Computers Printed Article, Vol. 37, 2020 No. 12
Section: Technology of Graphic & Image
Pages: 3777-3780,3835
Serial Number: 1001-3695(2020)12-053-3777-04

Publish History

[2020-12-05] Printed Article

Cite This Article

赖振意, 陈人和, 钱育蓉. 结合空洞卷积的CNN实时微表情识别算法 [J]. 计算机应用研究, 2020, 37 (12): 3777-3780,3835. (Lai Zhenyi, Chen Renhe, Qian Yurong. Real-time micro-expression recognition algorithm based on atrous convolutions for CNN [J]. Application Research of Computers, 2020, 37 (12): 3777-3780,3835. )

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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