Isolated Sign Language Recognition (ISLR) aims to classify signs into the corresponding gloss, but it remains challenging due to rapid movements and minute changes of hands. Pose-based approaches, recently gaining attention due to their robustness against the environment, are crucial against such challenging movements and changes due to the diculty of capturing small joint movements from the noisy keypoints. In this work, we emphasize the importance of preprocessing keypoints to alleviate the risk of such errors. We employ normalization using anchor points to accurately track the relative motion of skeletal joints, focusing on hand movements. Additionally, we implement bilinear interpolation to reconstruct keypoints, particularly to retrieve missing information for hands that were not detected. Preprocessing methods proposed in this work show a 6.05% improvement in accuracy and achieved 83.26% accuracy with data augmentation on the WLASL dataset, which is the highest among pose-based approaches. The proposed methods show strengths in cases with signs having importance in the hand shape, especially when some frames have undetected hands.
@inproceedings{roh:24052:sign-lang:lrec,
author = {Roh, Kyunggeun and Lee, Huije and Hwang, Eui Jun and Cho, Sukmin and Park, Jong C.},
title = {Preprocessing Mediapipe Keypoints with Keypoint Reconstruction and Anchors for Isolated Sign Language Recognition},
pages = {400--411},
editor = {Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Mesch, Johanna and Schulder, Marc},
booktitle = {Proceedings of the {LREC-COLING} 2024 11th Workshop on the Representation and Processing of Sign Languages: Evaluation of Sign Language Resources},
maintitle = {2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation ({LREC-COLING} 2024)},
publisher = {{ELRA Language Resources Association (ELRA) and the International Committee on Computational Linguistics (ICCL)}},
address = {Torino, Italy},
day = {25},
month = may,
year = {2024},
isbn = {978-2-493814-30-2},
language = {english},
url = {https://www.sign-lang.uni-hamburg.de/lrec/pub/24052.pdf}
}
| Lee, Huije