Abstract
Sign languages are visual languages produced by the movement of the hands, face, and body. In this paper, we evaluate representations based on skeleton poses, as these are explainable, person-independent, privacy-preserving, low-dimensional representations. Basically, skeletal representations generalize over an individual's appearance and background, allowing us to focus on the recognition of motion. But how much information is lost by the skeletal representation? We perform two independent studies using two state-of-the-art pose estimation systems. We analyze the applicability of the pose estimation systems to sign language recognition by evaluating the failure cases of the recognition models. Importantly, this allows us to characterize the current limitations of skeletal pose estimation approaches in sign language recognition.
Original language | English |
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Title of host publication | Proceedings - 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021 |
Publisher | IEEE Computer Society |
Pages | 3429-3435 |
Number of pages | 7 |
ISBN (Electronic) | 9781665448994 |
DOIs | |
State | Published - Jun 2021 |
Event | 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021 - Virtual, Online, United States Duration: 19 Jun 2021 → 25 Jun 2021 |
Publication series
Name | IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops |
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ISSN (Print) | 2160-7508 |
ISSN (Electronic) | 2160-7516 |
Conference
Conference | 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021 |
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Country/Territory | United States |
City | Virtual, Online |
Period | 19/06/21 → 25/06/21 |
Bibliographical note
Publisher Copyright:© 2021 IEEE.