research-article

Isolated Sign Language Recognition with Grassmann Covariance Matrices

Authors:

Xilin ChenAuthors Info & Claims

ACM Transactions on Accessible Computing (TACCESS), Volume 8, Issue 4

Article No.: 14, Pages 1 - 21

https://doi.org/10.1145/2897735

Published: 07 May 2016 Publication History

Abstract

In this article, to utilize long-term dynamics over an isolated sign sequence, we propose a covariance matrix--based representation to naturally fuse information from multimodal sources. To tackle the drawback induced by the commonly used Riemannian metric, the proximity of covariance matrices is measured on the Grassmann manifold. However, the inherent Grassmann metric cannot be directly applied to the covariance matrix. We solve this problem by evaluating and selecting the most significant singular vectors of covariance matrices of sign sequences. The resulting compact representation is called the Grassmann covariance matrix. Finally, the Grassmann metric is used to be a kernel for the support vector machine, which enables learning of the signs in a discriminative manner. To validate the proposed method, we collect three challenging sign language datasets, on which comprehensive evaluations show that the proposed method outperforms the state-of-the-art methods both in accuracy and computational cost.

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Cited By

Wang ZLi DJiang ROkumura M(2025)Continuous Sign Language Recognition With Multi-Scale Spatial-Temporal Feature EnhancementIEEE Access10.1109/ACCESS.2025.352633013(5491-5506)Online publication date: 2025
https://doi.org/10.1109/ACCESS.2025.3526330
Liu ZFang GWang WLiu Q(2024)Multimodal Sign Language Knowledge Graph and Representation: Text Video KeyFrames and Motion TrajectoriesProceedings of the 6th ACM International Conference on Multimedia in Asia10.1145/3696409.3700208(1-7)Online publication date: 3-Dec-2024
https://dl.acm.org/doi/10.1145/3696409.3700208
Liu ZPang LQi X(2024)MEN: Mutual Enhancement Networks for Sign Language Recognition and EducationIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.317403135:1(311-325)Online publication date: Jan-2024
https://doi.org/10.1109/TNNLS.2022.3174031
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Index Terms

Isolated Sign Language Recognition with Grassmann Covariance Matrices
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
        Activity recognition and understanding

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Published In

cover image ACM Transactions on Accessible Computing

ACM Transactions on Accessible Computing Volume 8, Issue 4

May 2016

80 pages

ISSN:1936-7228

EISSN:1936-7236

DOI:10.1145/2905046

Editors:
Matt Huenerfauth
Rochester Institute of Technology, USA
,
Kathleen F. McCoy
University of Delaware, USA

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Copyright © 2016 ACM.

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Association for Computing Machinery

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Publication History

Published: 07 May 2016

Accepted: 01 February 2016

Revised: 01 February 2016

Received: 01 May 2015

Published in TACCESS Volume 8, Issue 4

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Microsoft Research Asia and the Natural Science Foundation of China
Infotech Oulu
Academy of Finland
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Cited By

Wang ZLi DJiang ROkumura M(2025)Continuous Sign Language Recognition With Multi-Scale Spatial-Temporal Feature EnhancementIEEE Access10.1109/ACCESS.2025.352633013(5491-5506)Online publication date: 2025
https://doi.org/10.1109/ACCESS.2025.3526330
Liu ZFang GWang WLiu Q(2024)Multimodal Sign Language Knowledge Graph and Representation: Text Video KeyFrames and Motion TrajectoriesProceedings of the 6th ACM International Conference on Multimedia in Asia10.1145/3696409.3700208(1-7)Online publication date: 3-Dec-2024
https://dl.acm.org/doi/10.1145/3696409.3700208
Liu ZPang LQi X(2024)MEN: Mutual Enhancement Networks for Sign Language Recognition and EducationIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.317403135:1(311-325)Online publication date: Jan-2024
https://doi.org/10.1109/TNNLS.2022.3174031
Diba BPlabon JMahmudur Rahman MMistry DSaha AMridha M(2024)Explainable federated learning for privacy-preserving bangla sign language detectionEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.108657134(108657)Online publication date: Aug-2024
https://doi.org/10.1016/j.engappai.2024.108657
Gao LFeng WLyu FWan L(2024)Combinational sign language recognitionComputer Vision and Image Understanding10.1016/j.cviu.2024.103972(103972)Online publication date: Feb-2024
https://doi.org/10.1016/j.cviu.2024.103972
Miao QLi YLiu XLiu R(2024)Common datasets in the field of gesture recognitionGesture Recognition10.1016/B978-0-443-28959-0.00008-X(17-33)Online publication date: 2024
https://doi.org/10.1016/B978-0-443-28959-0.00008-X
Hüseyinoǧlu ABilge FBilge YIkizler-Cinbis N(2024)Tinysign: sign language recognition in low resolution settingsSignal, Image and Video Processing10.1007/s11760-024-03358-z18:10(6881-6890)Online publication date: 28-Jun-2024
https://doi.org/10.1007/s11760-024-03358-z
Zhang PYin HWang ZChen WLi SWang DLu HJia X(2024)EvSign: Sign Language Recognition and Translation with Streaming EventsComputer Vision – ECCV 202410.1007/978-3-031-72652-1_20(335-351)Online publication date: 30-Oct-2024
https://doi.org/10.1007/978-3-031-72652-1_20
Begum NRahman RJahan NKhan SHelaly THaque AKhatun N(2023)Borno-Net: A Real-Time Bengali Sign-Character Detection and Sentence Generation System Using Quantized Yolov4-Tiny and LSTMsApplied Sciences10.3390/app1309521913:9(5219)Online publication date: 22-Apr-2023
https://doi.org/10.3390/app13095219
Kajiyama TShishikui Y(2023)Evaluation of Sign Language Recognition with Higher Resolution Face Images顔表情を解析する手話認識における画像解像度と認識精度の評価The Journal of The Institute of Image Information and Television Engineers10.3169/itej.77.78877:6(788-794)Online publication date: 2023
https://doi.org/10.3169/itej.77.788
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