Recent advances of deep learning for sign language recognition

Lihong Zheng; Bin Liang; Ailian Jiang

doi:10.1109/DICTA.2017.8227483

Recent advances of deep learning for sign language recognition

Lihong Zheng, Bin Liang, Ailian Jiang

Taiyuan University of Technology

Research output: Book chapter/Published conference paper › Conference paper › peer-review

37 Citations (Scopus)

Abstract

To assist the social interaction of deaf and hearing impaired people, efﬁcient interactive communication tools is expected. With the growing research interest in action and gesture recognition in the recent years, many successful applications for sign language recognition comprise new types of sensors including low-cost depth camera and advanced machine learning technologies. In this paper, we present a complete overview of deep learning based methodologies for sign language recognition. We discuss various types of such approaches designed for the recognition from viewpoints of available modalities provided by depth sensors, feature extraction and classiﬁcation. In addition, we summarise the currently available datasets of sign language, including gestures of ﬁnger spelling and vocabulary words, which can be used as an assessing tool for those people who are learning sign languages. We then discuss the main current research works with particular interest on how they treat the different types of data, discussing their main features and identify opportunities and challenges for future research.

Original language	English
Title of host publication	Proceedings of the 2017 international conference on Digital Image Computing: Techniques and Applications
Place of Publication	United States
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	1-7
Number of pages	7
ISBN (Electronic)	9781538628393
ISBN (Print)	9781538628409 (Print on demand)
DOIs	https://doi.org/10.1109/DICTA.2017.8227483
Publication status	Published - 21 Dec 2017
Event	2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA) - Novotel Sydney Manly Pacific, Sydney, Australia Duration: 29 Nov 2017 → 01 Dec 2017 http://dicta2017.dictaconference.org/index.html (Conference website) http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8226656 (Conference proceedings)

Conference

Conference	2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA)
Country/Territory	Australia
City	Sydney
Period	29/11/17 → 01/12/17
Other	The International Conference on Digital Image Computing: Techniques and Applications (DICTA) is the main Australian Conference on computer vision, image processing, pattern recognition, and related areas. DICTA was established in 1991 as the premier conference of the Australian Pattern Recognition Society (APRS).
Internet address	http://dicta2017.dictaconference.org/index.html (Conference website) http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8226656 (Conference proceedings)

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10.1109/DICTA.2017.8227483

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title = "Recent advances of deep learning for sign language recognition",

abstract = "To assist the social interaction of deaf and hearing impaired people, efﬁcient interactive communication tools is expected. With the growing research interest in action and gesture recognition in the recent years, many successful applications for sign language recognition comprise new types of sensors including low-cost depth camera and advanced machine learning technologies. In this paper, we present a complete overview of deep learning based methodologies for sign language recognition. We discuss various types of such approaches designed for the recognition from viewpoints of available modalities provided by depth sensors, feature extraction and classiﬁcation. In addition, we summarise the currently available datasets of sign language, including gestures of ﬁnger spelling and vocabulary words, which can be used as an assessing tool for those people who are learning sign languages. We then discuss the main current research works with particular interest on how they treat the different types of data, discussing their main features and identify opportunities and challenges for future research. ",

keywords = "Deep learning, Classification, Feature extraction, Sign language recognition, Depth images",

author = "Lihong Zheng and Bin Liang and Ailian Jiang",

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year = "2017",

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booktitle = "Proceedings of the 2017 international conference on Digital Image Computing: Techniques and Applications",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

address = "United States",

url = "http://dicta2017.dictaconference.org/index.html, http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8226656",

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Zheng, L, Liang, B & Jiang, A 2017, Recent advances of deep learning for sign language recognition. in Proceedings of the 2017 international conference on Digital Image Computing: Techniques and Applications., 8227483, IEEE, Institute of Electrical and Electronics Engineers, United States, pp. 1-7, 2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA), Sydney, New South Wales, Australia, 29/11/17. https://doi.org/10.1109/DICTA.2017.8227483

Recent advances of deep learning for sign language recognition. / Zheng, Lihong; Liang, Bin; Jiang, Ailian.
Proceedings of the 2017 international conference on Digital Image Computing: Techniques and Applications. United States: IEEE, Institute of Electrical and Electronics Engineers, 2017. p. 1-7 8227483.

Research output: Book chapter/Published conference paper › Conference paper › peer-review

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AU - Liang, Bin

AU - Jiang, Ailian

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N2 - To assist the social interaction of deaf and hearing impaired people, efﬁcient interactive communication tools is expected. With the growing research interest in action and gesture recognition in the recent years, many successful applications for sign language recognition comprise new types of sensors including low-cost depth camera and advanced machine learning technologies. In this paper, we present a complete overview of deep learning based methodologies for sign language recognition. We discuss various types of such approaches designed for the recognition from viewpoints of available modalities provided by depth sensors, feature extraction and classiﬁcation. In addition, we summarise the currently available datasets of sign language, including gestures of ﬁnger spelling and vocabulary words, which can be used as an assessing tool for those people who are learning sign languages. We then discuss the main current research works with particular interest on how they treat the different types of data, discussing their main features and identify opportunities and challenges for future research.

AB - To assist the social interaction of deaf and hearing impaired people, efﬁcient interactive communication tools is expected. With the growing research interest in action and gesture recognition in the recent years, many successful applications for sign language recognition comprise new types of sensors including low-cost depth camera and advanced machine learning technologies. In this paper, we present a complete overview of deep learning based methodologies for sign language recognition. We discuss various types of such approaches designed for the recognition from viewpoints of available modalities provided by depth sensors, feature extraction and classiﬁcation. In addition, we summarise the currently available datasets of sign language, including gestures of ﬁnger spelling and vocabulary words, which can be used as an assessing tool for those people who are learning sign languages. We then discuss the main current research works with particular interest on how they treat the different types of data, discussing their main features and identify opportunities and challenges for future research.

KW - Deep learning

KW - Classification

KW - Feature extraction

KW - Sign language recognition

KW - Depth images

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Recent advances of deep learning for sign language recognition

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