计算机科学
水准点(测量)
步态
人工智能
特征提取
特征(语言学)
模式识别(心理学)
对偶(语法数字)
过程(计算)
接头(建筑物)
领域(数学)
操作系统
地理
生物
工程类
纯数学
大地测量学
数学
文学类
哲学
语言学
建筑工程
艺术
生理学
作者
Feixiang Zhang,Xiao Sun
标识
DOI:10.1007/978-3-031-46305-1_16
摘要
Gait is a distinctive human feature that can be recognized from a distance and has been widely utilized in the field of emotion recognition. In this study, we propose a novel dual-stream model (GLM) for gait emotion recognition that combines the strengths of global and local features. We extract skeleton point gait data from walking videos and process them into suitable inputs for two channels of feature extraction networks, which respectively capture global and local characteristics. To enhance the features and improve recognition accuracy, we further introduce an attention-based feature fusion module. Through experiments on benchmark datasets, our proposed model achieves high accuracy in recognizing emotions from gait data.
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