NDLI: Automatic Recognition of Colloquial Australian Sign Language

Content Provider	IEEE Xplore Digital Library
Author	Holden, Eun-Jung Lee, Gareth Owens, Robyn
Copyright Year	2005
Description	Author affiliation: The University of Western Australia (Holden, Eun-Jung; Lee, Gareth; Owens, Robyn)
Abstract	This paper presents an automatic Australian sign language (Auslan) recognition system, which tracks multiple target objects (the face and hands) throughout an image sequence and extracts features for the recognition of sign phrases. Tracking is performed using correspondences of simple geometrical features between the target objects within the current and the previous frames. In signing, the face and a hand of a signer often overlap, thus the system needs to segment these for the purpose of feature extraction. Our system deals with the occlusion of the face and a hand by detecting the contour of the foreground moving object using a combination of motion cues and the snake algorithm. To represent signs, features that are invariant to scaling, 2D rotations, and signing speed are used for recognition. The features represent the relative geometrical positioning and shapes of the target objects, as well as their directions of motion. These are used to recognise Auslan phrases using Hidden Markov Models. Experiments were conducted using 163 test sign phrases with varying grammatical formations. Using a known grammar, the system achieved over 97% recognition rate on a sentence level and 99% success rate at a word level.
Starting Page	183
Ending Page	188
File Size	239736
Page Count	6
File Format	PDF
ISBN	0769522718
DOI	10.1109/ACVMOT.2005.30
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-01-05
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Australia Handicapped aids Face detection Face recognition Image recognition Target recognition Target tracking Feature extraction Image sequences Image segmentation
Content Type	Text
Resource Type	Article

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