基于局部特征聚类损失和多类特征融合的面部表情识别

doi:10.16451/j.cnki.issn1003-6059.202003008

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Abstract Each individual makes facial expressions in a unique way. In this paper, a locality-feature-aggregation(LFA) loss function is proposed. Differences between images of the same class are reduced and those between images of different classes are expanded during the training of deep neural network. Thus, the influence of expression polymorphism on feature extraction by deep learning is weakened. The local areas with rich expressions can express facial expression features better. A deep learning network framework incorporating LFA loss function is proposed. Local features of facial images are extracted for facial expression recognition. Compared with other methods, the proposed method is more effective on real world RAF datasets and CK+ datasets under laboratory conditions.

Key words： Facial Expression Recognition Convolutional Neural Network Deep Learning Local Features

Received: 28 October 2019

ZTFLH:

TP 391.41

Fund:Supported by National Natural Science Foundation of China(No.61872327), Fundamental Research Funds for Central Universities(No.ACAIM190102), Project of Innovation Team of Ministry of Education of China(No.IRT17R32), Natural Science Foundation of Anhui Province(No.1708085MF146), Project of Collaborative Innovation in Anhui Colleges and Universities(No.GXXT-2019-003)

Corresponding Authors: FANG Baofu, Ph.D., professor. His research interests include machine learning, machine vision and multi-robot systems.

About author:: WANG Hao, Ph.D., professor. His research interests include intelligent computing theory and software,artificial intelligence, machine vision and data mining. LI Yongze, master student. His research interests include computer application techno-logy, facial expression recognition and machine learning.

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Cite this article:

WANG Hao,LI Yongze,FANG Baofu. Locality Feature Aggregation Loss and Multi-feature Fusion for Facial Expression Recognition[J]. , 2020, 33(3): 268-276.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202003008 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2020/V33/I3/268

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