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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.png)
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摘要
En 中文
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. (C) 2018 Elsevier B.V. All rights reserved.
Keyword:
Micro-expression
Deep learning
Transferring learning
Convolutional neural network
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期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
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Spontaneous facial micro-expression analysis using Spatiotemporal Completed Local Quantized Patterns.png)
NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
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