基于三维卷积神经网络和峰值帧光流的微表情识别算法

doi:10.16451/j.cnki.issn1003-6059.202105005

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Abstract The existing micro-expression recognition technologies cannot make full use of the spatiotemporal features near the apex frame. Aiming at this problem, a micro-expression recognition algorithm based on 3D convolutional neural network and optical flow fields from the neighboring frames of the apex frame is proposed. Firstly, the optical flow fields between the adjacent frames before and after the apex frame are extracted. The important spatiotemporal information of micro-expressions are retained while the redundant information is removed and the computation load is reduced. Then, a 3D convolutional neural network is employed to extract the enhanced spatiotemporal features from the optical flow fields and thus the classification is completed. Finally, experiments on three spontaneous micro-expression databases show the proposed algorithm produces a better accuracy.

Key words： Micro-Expression Recognition 3D Convolutional Neural Network Optical Flow Apex Frame

Received: 09 October 2020

ZTFLH:

TP 391

Fund:Beijing Natural Science Foundation(No.4162056),Fundamental Research Funds for the Central Universities(No.2016MS33)

Corresponding Authors: JIA Jingping, Ph.D., lecturer. His research interests include computer vision, machine learning and cybersecurity.

About author:: ZHANG Xuesen, master student. His research interests include computer vision.

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ZHANG Xuesen,JIA Jingping. Micro-Expression Recognition Algorithm Based on 3D Convolutional Neural Network and Optical Flow Fields from Neighboring Frames of Apex Frame[J]. , 2021, 34(5): 423-433.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202105005 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2021/V34/I5/423

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