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Dynamic Facial Stress Recognition in Temporal Convolutional Network

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Neural Information Processing (ICONIP 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1142))

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Abstract

Stress is a major problem that infiltrates our society in countless ways. We cannot eliminate stress, but can recognize stress and manage it. Automatically recognizing stress through facial expressions has been extensively studied in the past decades. Recent research indicates that certain architectures can reach state-of-the-art accuracy in stress recognition. However, they recognise facial stress in view of static expressions, while only a few papers identify the fundamental limitations of static facial expression. This paper adapts ANUStressDB database in dynamic and develops a Temporal Convolutional Network to recognize continuous facial stress problem. We further apply Bimodal Distribution Removal to improve our result. The experimental results show that our system achieves 67.56% classification accuracy.

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Authors and Affiliations

  1. Research School of Computer Science, Australian National University, Canberra, Australia

    Sidong Feng

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  1. Sidong Feng
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Correspondence to Sidong Feng .

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Editors and Affiliations

  1. Australian National University, Canberra, ACT, Australia

    Tom Gedeon

  2. Murdoch University, Murdoch, WA, Australia

    Kok Wai Wong

  3. Kyungpook National University, Daegu, Korea (Republic of)

    Minho Lee

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Feng, S. (2019). Dynamic Facial Stress Recognition in Temporal Convolutional Network. In: Gedeon, T., Wong, K., Lee, M. (eds) Neural Information Processing. ICONIP 2019. Communications in Computer and Information Science, vol 1142. Springer, Cham. https://doi.org/10.1007/978-3-030-36808-1_76

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  • DOI: https://doi.org/10.1007/978-3-030-36808-1_76

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-36807-4

  • Online ISBN: 978-3-030-36808-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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