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Evaluating the Immediate Applicability of Pose Estimation for Sign Language Recognition


Moryossef, Amit; Tsochantaridis, Ioannis; Dinn, Joe; Camgöz, Necati Cihan; Bowden, Richard; Jiang, Tao; Rios, Annette; Müller, Mathias; Ebling, Sarah (2021). Evaluating the Immediate Applicability of Pose Estimation for Sign Language Recognition. arXiv.org 10166v1, University of Zurich.

Abstract

Signed 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.

Abstract

Signed 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.

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Additional indexing

Item Type:Working Paper
Communities & Collections:06 Faculty of Arts > Institute of Computational Linguistics
Dewey Decimal Classification:000 Computer science, knowledge & systems
410 Linguistics
Language:English
Date:2021
Deposited On:25 May 2021 14:53
Last Modified:25 Oct 2021 16:18
Series Name:arXiv.org
ISSN:2331-8422
Additional Information:2021 ChaLearn Looking at People Sign Language Recognition in the Wild Workshop at CVPR
OA Status:Green
Free access at:Official URL. An embargo period may apply.
Official URL:https://arxiv.org/abs/2104.10166
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