@proceedings {134,
	title = {Towards a visual Sign Language dataset for home care services},
	journal = {15th IEEE International Conference on Face and Gesture Recognition},
	volume = {1},
	year = {2020},
	month = {2020},
	pages = {622-626},
	abstract = {[size= 13.008px]We present our work towards creating a dataset, which is intended to be used for the implementation of a home care services system for the deaf. The dataset includes recorded realistic scenarios of interactions between deaf patients and mental health experts in their native sign language. The scenarios allow for contextualized representations, in contrast to typical datasets presenting isolated signs or sentences. It includes continuous videos in RGB and depth, which are challenging to analyze and closely resemble real-life scenarios. The research on representation of signs is supported by providing the hand shapes and trajectories for every video using hand and skeleton models, as well as facial features. Furthermore, the dataset may be used for the study of emotional context in Sign Language, since such conversations are typically emotionally charged. [/size]},
	author = {D. Kosmopoulos and I. Oikonomidis and K. Konstantinopoulos and N. Arvanitis and K. Antzakas and A. Bifis and G. Lydakis and A. Roussos and A. Argyros}
}
@proceedings {133,
	title = {Translation of Sign Language Glosses to Text Using Sequence-to-Sequence Attention Models},
	journal = {The 15th International Conference on Signal Image Technology \& Internet based Systems - SITIS},
	year = {2019},
	month = {2019},
	abstract = {This work deals with the problem of Sign Language Translation and more specifically with translating Glosses to text. We applied Sequence to Sequence models with attention mechanism to a parallel gloss to English corpus. This is the first work that used these models to translate American gloss sentences to English. We present our experiments on several network architectures with three different attention functions. The results are very promising and can be useful for the further implementation of a full sign language recognition system.},
	author = {N. Arvanitis and C. Constantinopoulos and D. Kosmopoulos}
}
