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The Korean Sign Language Dataset for Action Recognition

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Book cover MultiMedia Modeling (MMM 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11961))

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Abstract

Recently, the development of computer vision technologies has shown excellent performance in complex tasks such as behavioral recognition. Therefore, several studies propose datasets for behavior recognition, including sign language recognition. In many countries, researchers are carrying out studies to automatically recognize and interpret sign language to facilitate communication with deaf people. However, there is no dataset aiming at sign language recognition that is used in Korea yet, and research on this is insufficient. Since sign language varies from country to country, it is valuable to build a dataset for Korean sign language. Therefore, this paper aims to propose a dataset of videos of isolated signs from Korean sign language that can also be used for behavior recognition using deep learning. We present the Korean Sign Language (KSL) dataset. The dataset is composed of 77 words of Korean sign language video clips conducted by 20 deaf people. We train and evaluate this dataset in deep learning networks that have recently achieved excellent performance in the behavior recognition task. Also, we have confirmed through the deconvolution-based visualization method that the deep learning network fully understands the characteristics of the dataset.

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Author information

Authors and Affiliations

  1. Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea

    Seunghan Yang, Seungjun Jung & Changick Kim

  2. Samsung Electornics, Suwon, Korea

    Heekwang Kang

Authors
  1. Seunghan Yang
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  2. Seungjun Jung
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  3. Heekwang Kang
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  4. Changick Kim
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Corresponding author

Correspondence to Changick Kim .

Editor information

Editors and Affiliations

  1. Korea Advanced Institute of Science and, Daejeon, Korea (Republic of)

    Yong Man Ro

  2. National Chiao Tung University, Hsinchu, Taiwan

    Wen-Huang Cheng

  3. Korea Advanced Institute of Science and Technology, Daejeon, Korea (Republic of)

    Junmo Kim

  4. National Cheng Kung University, Tainan City, Taiwan

    Wei-Ta Chu

  5. Tsinghua University, Beijing, China

    Peng Cui

  6. Korea Advanced Institute of Science and Technology, Daejeon, Korea (Republic of)

    Jung-Woo Choi

  7. National Tsing Hua University, Hsinchu, Taiwan

    Min-Chun Hu

  8. Ghent University, Ghent, Belgium

    Wesley De Neve

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Yang, S., Jung, S., Kang, H., Kim, C. (2020). The Korean Sign Language Dataset for Action Recognition. In: Ro, Y., et al. MultiMedia Modeling. MMM 2020. Lecture Notes in Computer Science(), vol 11961. Springer, Cham. https://doi.org/10.1007/978-3-030-37731-1_43

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  • DOI: https://doi.org/10.1007/978-3-030-37731-1_43

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-37730-4

  • Online ISBN: 978-3-030-37731-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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