Dual Temporal Scale Convolutional Neural Network for Micro-Expression Recognition

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ID: 71456
2017
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Ranked #219 of 510 articles by views in Frontiers in psychology

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Abstract
Facial micro-expression is a brief involuntary facial movement and can reveal the genuine emotion that people try to conceal. Traditional methods of spontaneous micro-expression recognition rely excessively on sophisticated hand-crafted feature design and the recognition rate is not high enough for its practical application. In this paper, we proposed a Dual Temporal Scale Convolutional Neural Network (DTSCNN) for spontaneous micro-expressions recognition. The DTSCNN is a two-stream network. Different of stream of DTSCNN is used to adapt to different frame rate of micro-expression video clips. Each stream of DSTCNN consists of independent shallow network for avoiding the overfitting problem. Meanwhile, we fed the networks with optical-flow sequences to ensure that the shallow networks can further acquire higher-level features. Experimental results on spontaneous micro-expression databases (CASME I/II) showed that our method can achieve a recognition rate almost 10% higher than what some state-of-the-art method can achieve.
Reference Key
peng2017dualfrontiers Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Peng, Min;Peng, Min;Wang, Chongyang;Wang, Chongyang;Chen, Tong;Chen, Tong;Chen, Tong;Liu, Guangyuan;Liu, Guangyuan;Fu, Xiaolan;
Journal Frontiers in psychology
Year 2017
DOI
DOI not found
URL
http://journal.frontiersin.org/article/10.3389/fpsyg.2017.01745/full
Keywords
Pharmacy and materia medica Science physics psychology business history of scholarship and learning. the humanities commerce law computer engineering. computer hardware control engineering systems. automatic machinery (general) astrophysics

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