计算机工程与应用2025，Vol.61Issue(18)：142-156,15.DOI:10.3778/j.issn.1002-8331.2403-0291
关键点引导与显著帧增强的情感识别网络
Key-Points Guidance and Significant-Frames Enhancement for Emotion Recognition Network
摘要
Abstract
Aiming at the problems of spatial proportion difference and temporal peak asynchrony of emotion cues between facial expression and bodily posture,a key-points guidance and significant-frames enhancement(KGSE-ER)net-work is proposed for emotion recognition.In the spatial key-points guidance subnet,to capture the intra-frame emotional correlation and complementary information between facial expression and bodily posture,a spatial key-points guidance mechanism is constructed to obtain facial expression guidance semantics and bodily posture guidance semantics based on a cross-modal attention and a residual structure.In the temporal significant-frames enhancement subnet,to reduce the inter-frame redundant information caused by temporal peak asynchrony between facial expression and bodily posture,the emotional discrimination and dispersion are measured according to the facial expression guidance semantics and the bodily posture guidance semantics,and a temporal significant-frames enhancement strategy is proposed to enhance spatio-temporal features of cue-guided semantic sequences.The experimental results on FABO and CAER video datasets show that the emotion recognition accuracy of the proposed network reaches 95.31%and 89.78%respectively,which is 11.50 and 13.66 percentage points higher than the baseline network.Compared with related methods,the proposed network has better emotion recognition performance on both natural scene dynamic video datasets and static image datasets.关键词
面部表情/肢体姿态/情感识别/关键点引导/显著帧增强Key words
facial expression/bodily posture/emotion recognition/key-points guidance/significant-frames enhancemen分类
信息技术与安全科学引用本文复制引用
黄忠,张丹妮,任福继,胡敏,刘娟..关键点引导与显著帧增强的情感识别网络[J].计算机工程与应用,2025,61(18):142-156,15.基金项目
国家自然科学基金(62176084) （62176084）
安徽省自然科学基金(1908085MF195) （1908085MF195）
安徽省高校优秀青年人才支持计划项目(gxyqZD2021122) （gxyqZD2021122）
安徽省教育厅自然科学重点研究项目(2022AH051038,2023AH050490). （2022AH051038,2023AH050490）
