Automatic and unconstrained sign language recognition (SLR) in image sequences remains a challenging problem. The variety of signers, backgrounds, sign executions and signer positions makes the development of SLR systems very challenging. Current methods try to alleviate this complexity by extracting engineered features to detect hand shapes, hand trajectories and facial expressions as an intermediate step for SLR. Our goal is to approach SLR based on feature learning rather than feature engineering. We tackle SLR using the recent advances in the domain of deep learning with deep neural networks. The problem is approached by classifying isolated signs from the Corpus VGT (Flemish Sign Language Corpus) and the Corpus NGT (Dutch Sign Language Corpus). Furthermore, we investigate cross-domain feature learning to boost the performance to cope with the fewer Corpus VGT annotations.
@inproceedings{pigou:16011:sign-lang:lrec,
author = {Pigou, Lionel and Van Herreweghe, Mieke and Dambre, Joni},
title = {Sign Classification in Sign Language Corpora with Deep Neural Networks},
pages = {175--178},
editor = {Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Kristoffersen, Jette and Mesch, Johanna},
booktitle = {Proceedings of the {LREC2016} 7th Workshop on the Representation and Processing of Sign Languages: Corpus Mining},
maintitle = {10th International Conference on Language Resources and Evaluation ({LREC} 2016)},
publisher = {{European Language Resources Association (ELRA)}},
address = {Portoro{\v z}, Slovenia},
day = {28},
month = may,
year = {2016},
language = {english},
url = {https://www.sign-lang.uni-hamburg.de/lrec/pub/16011.pdf}
}