推论
计算机科学
人工智能
建筑
变压器
深度学习
模式识别(心理学)
编码器
机器学习
工程类
操作系统
电气工程
艺术
视觉艺术
电压
作者
Liangfei Zhang,Xiaopeng Hong,Ognjen Arandjelović,Guoying Zhao
出处
期刊:IEEE Transactions on Affective Computing
[Institute of Electrical and Electronics Engineers]
日期:2022-10-01
卷期号:13 (4): 1973-1985
被引量:30
标识
DOI:10.1109/taffc.2022.3213509
摘要
Being spontaneous, micro-expressions are useful in the inference of a person's true emotions even if an attempt is made to conceal them. Due to their short duration and low intensity, the recognition of micro-expressions is a difficult task in affective computing. The early work based on handcrafted spatio-temporal features which showed some promise, has recently been superseded by different deep learning approaches which now compete for the state of the art performance. Nevertheless, the problem of capturing both local and global spatio-temporal patterns remains challenging. To this end, herein we propose a novel spatio-temporal transformer architecture – to the best of our knowledge, the first purely transformer based approach (i.e., void of any convolutional network use) for micro-expression recognition. The architecture comprises a spatial encoder which learns spatial patterns, a temporal aggregator for temporal dimension analysis, and a classification head. A comprehensive evaluation on three widely used spontaneous micro-expression data sets, namely SMIC-HS, CASME II and SAMM, shows that the proposed approach consistently outperforms the state of the art, and is the first framework in the published literature on micro-expression recognition to achieve the unweighted F1-score greater than 0.9 on any of the aforementioned data sets. The source code is available at https://github.com/Vision-Intelligence-and-Robots-Group/SLSTT .
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