Micro-expression recognition with small sample size by transferring long-term convolutional neural network
Micro-expression recognition with small sample size by transferring long-term convolutional neural network
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DOI：
10.1016/j.neucom.2018.05.107
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发表时间：
2018-10
期刊：
影响因子：
6
通讯作者：
Sujing Wang;Bing-Jun Li;Yong-Jin Liu;Wen-Jing Yan;Xinyu Ou;Xiaohua Huang;Feng Xu;Xiaolan Fu
中科院分区：
文献类型：
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作者：
Sujing Wang;Bing-Jun Li;Yong-Jin Liu;Wen-Jing Yan;Xinyu Ou;Xiaohua Huang;Feng Xu;Xiaolan Fu
微表情是测谎的重要线索之一。其最突出的特点是持续时间短、运动强度低。因此，高时空分辨率的视频剪辑比静态图像更需要提供足够的细节。另一方面，由于微表情数据的收集和编码困难，样本量较小。在本文中，我们仅使用560个微表情视频片段来评估所提出的网络模型：转移长期卷积神经网络(TLCNN)。TLCNN使用Deep CNN从每一帧微表情视频片段中提取特征，并将其反馈到长短期记忆(LSTM)中，LSTM学习微表情的时间序列信息。由于微表情数据样本量小，TLCNN采用了两个步骤的迁移学习：(1)从表情数据迁移，(2)从微表情视频片段的单帧迁移，这可以被视为“大数据”。对从三个自发数据库中收集的560个微表情视频片段进行了评估。实验结果表明，本文提出的TLCNN算法比现有的一些算法具有更好的性能。
Micro-expression is one of important clues for detecting lies. Its most outstanding characteristics include short duration and low intensity of movement. Therefore, video clips of high spatial-temporal resolution are much more desired than still images to provide sufficient details. On the other hand, owing to the difficulties to collect and encode micro-expression data, it is small sample size. In this paper, we use only 560 micro-expression video clips to evaluate the proposed network model: Transferring Long-term Convolutional Neural Network (TLCNN). TLCNN uses Deep CNN to extract features from each frame of micro-expression video clips, then feeds them to Long Short Term Memory (LSTM) which learn the temporal sequence information of micro-expression. Due to the small sample size of micro-expression data, TLCNN uses two steps of transfer learning: (1) transferring from expression data and (2) transferring from single frame of micro-expression video clips, which can be regarded as “big data”. Evaluation on 560 micro-expression video clips collected from three spontaneous databases is performed. The results show that the proposed TLCNN is better than some state-of-the-art algorithms.