Chieh-Chih Wang

Hand Posture Recognition Using Adaboost with SIFT
for Human Robot Interaction

Chieh-Chih Wang and Ko-Chih Wang

International Conference on Advanced Robotics, 2007

Abstract

Hand posture understanding is essential to human robot interaction. The existing hand detection approaches using a Viola-Jones detector have two fundamental issues, the degraded performance due to background noise in training images and the in-plane rotation variant detection. In this paper, a hand posture recognition system using the discrete Adaboost learning algorithm with Lowe's scale invariant feature transform (SIFT) features is proposed to tackle these issues simultaneously. In addition, we apply a sharing feature concept to increase the accuracy of multi-class hand posture recognition. The experimental results demonstrate that the proposed approach successfully recognizes three hand posture classes and can deal with the background noise issues. Our detector is in-plane rotation invariant, and achieves satisfactory multi-view hand detection.

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Bibtex

@inproceedings{Wang07b,
    author = {Chieh-Chih Wang and Co-Chih Wang },
    title = {Hand Posture Recognition Using Adaboost with SIFT for Human Robot Interaction},
    booktitle = {Proceedings of the International Conference on Advanced Robotics (ICAR'07)},
    address = {Jeju, Korea},
    month = {August},
    year = {2007},
}