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Real-time Vision-based Hand Gesture Recognition Using Haar-like Features

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3 Author(s)

This paper proposes a two level approach to solve the problem of real-time vision-based hand gesture classification. The lower level of the approach implements the posture recognition with Haar-like features and the AdaBoost learning algorithm. With this algorithm, real-time performance and high recognition accuracy can be obtained. The higher level implements the linguistic hand gesture recognition using a context-free grammar-based syntactic analysis. Given an input gesture, based on the extracted postures, the composite gestures can be parsed and recognized with a set of primitives and production rules.

Published in:

Instrumentation and Measurement Technology Conference Proceedings, 2007. IMTC 2007. IEEE

Date of Conference:

1-3 May 2007

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