NDLI: Improved face and hand tracking for sign language recognition

Content Provider	IEEE Xplore Digital Library
Author	Soontranon, N. Aramvith, S. Chalidabhongse, T.H.
Copyright Year	2005
Description	Author affiliation: Dept. of Electr. Eng., Chulalongkorn Univ., Bangkok, Thailand (Soontranon, N.; Aramvith, S.)
Abstract	In this paper, we develop face and hand tracking for sign language recognition system. The system is divided into two stages: the initial and tracking stages. In initial stage, we use the skin feature to localize face and hands of signer. The ellipse model on CbCr space is constructed and used to detect skin color. After the skin regions have been segmented, face and hand blobs are defined by using size and facial feature with the assumption that the movement of face is less than that of hands in this signing scenario. In tracking stage, the motion estimation is applied only hand blobs, in which first and second derivative are used to compute the position of prediction of hands. We observed that there are errors in the value of tracking position between two consecutive frames in which velocity has changed abruptly. To improve the tracking performance, our proposed algorithm compensates the error of tracking position by using adaptive search area to re-compute the hand blobs. Simulation results indicate our proposed algorithm can track face and hand with greater precision with negligible computational complexity increase.
Starting Page	141
Ending Page	146
File Size	245180
Page Count	6
File Format	PDF
ISBN	0769523153
DOI	10.1109/ITCC.2005.172
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-04-04
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Handicapped aids Face recognition Face detection Skin Tracking Motion estimation Deafness Auditory system Electronic mail Information technology motion estimation Hand tracking sign language ellipse model skin-color segmentation
Content Type	Text
Resource Type	Article

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