判别式
对偶(语法数字)
人工智能
计算机科学
模式识别(心理学)
相似性(几何)
特征选择
比例(比率)
表达式(计算机科学)
人工神经网络
选择(遗传算法)
特征(语言学)
机器学习
艺术
语言学
哲学
物理
文学类
量子力学
图像(数学)
程序设计语言
作者
Haoliang Zhou,Shucheng Huang,Jingting Li,Sujing Wang
出处
期刊:Entropy
[MDPI AG]
日期:2023-03-06
卷期号:25 (3): 460-460
被引量:6
摘要
Micro-expression recognition (MER) is challenging due to the difficulty of capturing the instantaneous and subtle motion changes of micro-expressions (MEs). Early works based on hand-crafted features extracted from prior knowledge showed some promising results, but have recently been replaced by deep learning methods based on the attention mechanism. However, with limited ME sample sizes, features extracted by these methods lack discriminative ME representations, in yet-to-be improved MER performance. This paper proposes the Dual-branch Attention Network (Dual-ATME) for MER to address the problem of ineffective single-scale features representing MEs. Specifically, Dual-ATME consists of two components: Hand-crafted Attention Region Selection (HARS) and Automated Attention Region Selection (AARS). HARS uses prior knowledge to manually extract features from regions of interest (ROIs). Meanwhile, AARS is based on attention mechanisms and extracts hidden information from data automatically. Finally, through similarity comparison and feature fusion, the dual-scale features could be used to learn ME representations effectively. Experiments on spontaneous ME datasets (including CASME II, SAMM, SMIC) and their composite dataset, MEGC2019-CD, showed that Dual-ATME achieves better, or more competitive, performance than the state-of-the-art MER methods.
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