Increasing the effect of fingers in fingerspelling hand shapes by thick edge detection and correlation with penalization

Authors:
Oğuz Altun;Songül Albayrak
Affiliations:
Computer Engineering Department, Yıldız Technical University, Yıldız, İstanbul, Türkiye;Computer Engineering Department, Yıldız Technical University, Yıldız, İstanbul, Türkiye
Venue:
PSIVT'06 Proceedings of the First Pacific Rim conference on Advances in Image and Video Technology
Year:
2006

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Abstract

Fingerspelling is used in sign language to spell out names of people and places for which there is no sign or for which the sign is not known. In this work we describe a method for increasing the effect of fingers in Fingerspelling hand shapes. Hand shape objects are obtained by extraction of representative frames, color segmentation in YCrCb space and angle of least inertia based fast alignment [1]. Thick edges of the hand shape objects are extracted with a distance to edge based method. Finally a calculation that penalizes similarity for not-corresponding pixels is employed to correlation based template matching. The experimental Turkish fingerspelling recognition system recognizes all 29 letters of the Turkish alphabet. The train video database is created by three signers, and has a set of 290 videos. The test video database is created by four signers, and has a set of 203 videos. Our methods achieve a success rate of 99%.