Detecting and Identifying Sign Languages through Visual Features Conference Paper

abstract

• 2016 IEEE. The popularity of video sharing sites has encouraged the creation and distribution of sign language (SL) content. Unfortunately, locating SL videos on a desired topic is not a straightforward task. Retrieval depends on the existence and correctness of metadata to indicate that the video contains SL. This problem gets worse when considering a particular type of sign language (e.g. American Sign Language - ASL, British Sign Language - BSL, French Sign Language - LSF, etc.), where metadata needs to be even more specific. To address this problem, we have expanded a previous SL classifier to distinguish videos in different SLs. The new classifier achieves an F1 score of 98% when discriminating between BSL and LSF videos with static backgrounds, and a 70% F1 score when distinguishing between ASL and BSL videos found on popular video sharing sites. Such accuracy with visual features alone is possible when comparing languages with one-handed and two-handed manual alphabets.

authors

• Gutierrez-Osuna, Ricardo
• Shipman, Frank

published proceedings

• PROCEEDINGS OF 2016 IEEE INTERNATIONAL SYMPOSIUM ON MULTIMEDIA (ISM)

author list (cited authors)

• Monteiro, C., Mathew, C. M., Gutierrez-Osuna, R., & Shipman, F.

complete list of authors

• Monteiro, Caio DD||Mathew, Christy Maria||Gutierrez-Osuna, Ricardo||Shipman, Frank

publication date

• January 2016

keywords

• Language Identification
• Sign Language Detection

Digital Object Identifier (DOI)

• 10.1109/ISM.2016.71

International Standard Book Number (ISBN) 13

• 9781509045709

start page

• 287

end page

• 290