基于注意增强LSTM神经网络的微表情识别

Shiqi Xu, Fen Xu
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引用次数: 0

摘要

微表情识别是计算机视觉中的一个难点。现有的微表情识别方法大多是全局提取面部特征,导致包含了许多不相关的特征,对识别精度产生了负面影响。本文设计了具有时空注意机制的长短期记忆(LSTM)神经网络,并将其用于从输入序列中选择性地提取特征。首先从原始微表情序列中识别关键帧。然后利用VGG-Face模型提取关键帧的空间特征。然后将微表情序列的空间特征输入注意增强长短期记忆神经网络,使用softmax函数进行最终分类。我们在CASME II上的实验表明,与其他几种领先方法的结果相比,注意力增强的LSTM模型显著提高了微表情识别的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Micro-expression Recognition Based on Attention-enhanced LSTM Neural Networks
Micro-expression recognition is a difficult task in computer vision. Most existing micro-expression recognition methods extract facial features globally, leading to the inclusion of many irrelevant features and affecting the recognition accuracy in a negative way. In this paper, Long Short-Term Memory (LSTM) neural networks with spatial and temporal attention mechanisms are designed and employed to extract features selectively from the input sequences. Key frames are identified from the original micro-expression sequences at first. Then the VGG-Face model is used to extract the spatial features of those key frames. The spatial features of the micro-expression sequences are then fed into attention-enhanced long short-term memory neural networks, using a softmax function for the final classification. Our experiments with CASME II show that the attention-enhanced LSTM models improve the accuracy of micro-expression recognition significantly, compared to the results of several other leading methods.
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