Journal
PATTERN RECOGNITION
Volume 41, Issue 5, Pages 1638-1652Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2007.10.016
Keywords
gesture recognition; sign language recognition; handshape recognition; motion trajectory recognition; linear discriminant analysis; clustering; vector quantization principal component analysis
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We present effective and robust algorithms to recognize isolated signs in Signing Exact English (SEE). The sign-level recognition scheme comprises classifiers for handshape, hand movement and hand location. The SEE gesture data are acquired using CyberGlove((R)) and magnetic trackers. A linear decision tree with Fisher's linear discriminant (FLD) is used to classify 27 SEE handshapes. Hand movement trajectory is classified using vector quantization principal component analysis (VQPCA). Both periodic and non-periodic SEE sign gestures are recognized from isolated 3-D hand trajectories. Experiments yielded average handshape recognition accuracy of 96.1% on unseen signers. The average trajectory recognition rate with VQPCA for non-periodic and periodic gestures was 97.3% and 97.0%, respectively. These classifiers were combined with a hand location classifier for sign-level recognition, yielding an accuracy of 86.8% on a 28 sign SEE vocabulary. (c) 2007 Elsevier Ltd. All rights reserved.
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