[1]叶耀光,陈宗楠,陈丽群,等.基于通道注意的可变形金字塔表情识别网络[J].计算机技术与发展,2022,32(11):64-71.[doi:10. 3969 / j. issn. 1673-629X. 2022. 11. 010]
　YE Yao-guang,CHEN Zong-nan,CHEN Li-qun,et al.Channel-attention-based Deformable Pyramid Network for Facial Expression Recognition[J].,2022,32(11):64-71.[doi:10. 3969 / j. issn. 1673-629X. 2022. 11. 010]
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基于通道注意的可变形金字塔表情识别网络()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
32
期数:
2022年11期
页码:
64-71
栏目:
媒体计算
出版日期:
2022-11-10

文章信息/Info

Title:
Channel-attention-based Deformable Pyramid Network for Facial Expression Recognition
文章编号:
1673-629X(2022)11-0064-08
作者:
叶耀光1 ; 陈宗楠1 ; 陈丽群1 ; 潘永琪2 ; 潘家辉1; 3
1. 华南师范大学 软件学院,广东 佛山 528225;
2. 华南农业大学 数学与信息学院 软件学院,广东 广州 510642;
3. 琶洲实验室,广东 广州 510320
Author(s):
YE Yao-guang1 ; CHEN Zong-nan1 ; CHEN Li-qun1 ; PAN Yong-qi2 ; PAN Jia-hui1 ; 3
1. School of Software,South China Normal University,Foshan 528225,China;
2. School of Mathematics and Informatics College of Software Engineering,South China Agricultural University,Guangzhou 510642,China;
3. Lab of Pazhou,Guangzhou 510320,China
关键词:
人脸表情识别; 卷积神经网络; 可变形卷积; 金字塔架构; 注意力机制
Keywords:
facial expression recognition; convolution neural network; deformable convolution; pyramid structure; attention mechanism
分类号:
TP391. 4
DOI:
10. 3969 / j. issn. 1673-629X. 2022. 11. 010
摘要:
人脸表情识别是计算机视觉领域里一项热门且有挑战性的任务。 由于人脸表情的特性相对固定,从宏观上针对人脸表情的特性进行方法设计能有效提高人脸表情识别的性能。 基于这一观点,针对人脸表情特征的不规则特性和空间多尺度互补特性,提出了基于通道注意的可变形金字塔网络。 该网络主要由可变形卷积块、空间金字塔池化块和通道注意块构成,其中可变形卷积块有助于网络对人脸表情的不规则特征进行采样;而空间金字塔池化块则加强了网络学习多尺度空间上下文情绪信息的能力;通道注意块则促使网络动态关注更具判别性的情绪特征。 该方法在 CK+、JAFFE 以及Oulu-CASIA 三个实验室环境的人脸表情数据集和 FER2013 以及 RAF-DB 两个野外环境的人脸表情数据集上进行了对比实验和消融实验并取得了有竞争力的结果。 从可视化结果上看,该方法提取的特征及关注的人脸区域符合不同表情的呈现特性和人们日常判断表情的规律。
Abstract:
Facial expression recognition is a popular and challenging task in the field of computer vision. Since the characteristics of facialexpressions are relatively fixed,designing methods for the characteristics of facial expressions from a macro perspective can effectivelyimprove the performance of facial expression recognition. Based on this view,a deformable pyramid network based on channel attentionis proposed for the irregular characteristics of facial expression features and the complementarity of spatial multi - scale. The networkmainly consists of deformable convolutional blocks, spatial pyramid pooling blocks and channel attention blocks. Specifically, thedeformable convolution block helps the network to sample irregular features of facial expressions,while the spatial pyramid pooling blockenhances the network’s ability to learn multi-scale spatial contextual emotion information,and the channel attention block motivates thenetwork to dynamically focus on more discriminative feature maps to improve the contribution of discriminative emotion information tothe facial expression recognition task. Experimental results on both three in-the-lab facial expression datasets ( including CK+,JAFFE,and Oulu-CASIA) and two in-the-wild facial expression datasets ( including FER2013 and RAF-DB) demonstrate the effectiveness ofthe proposed method. From the visualization results,the features extracted by the proposed method and the facial regions of interest areconsistent with the presentation characteristics of different expressions and the patterns of people’s daily judgment of expressions.

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更新日期/Last Update: 2022-11-10