Hand Posture Classification and Recognition using the Modified Census Transform

Developing new techniques for human-computer interaction is very challenging. Vision-based techniques have the advantage of being unobtrusive and hands are a natural device that can be used for more intuitive interfaces. But in order to use hands for interaction, it is necessary to be able to recognize them in images. In this paper, we propose to apply to the hand posture classification and recognition tasks an approach that has been successfully used for face detection~\cite{Froba04}. The features are based on the Modified Census Transform and are illumination invariant. For the classification and recognition processes, a simple linear classifier is trained, using a set of feature lookup-tables. The database used for the experiments is a benchmark database in the field of posture recognition. Two protocols have been defined. We provide results following these two protocols for both the classification and recognition tasks. Results are very encouraging.

Published in:
IEEE Int. Conf. on Automatic Face and Gesture Recognition (AFGR)
Presented at:
IEEE Int. Conf. on Automatic Face and Gesture Recognition (AFGR)
IDIAP-RR 06-02

 Record created 2006-03-10, last modified 2018-03-17

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