Hand Posture Recognition Using Adaboost with SIFT for Human Robot Interaction
Resource
Lecture Notes in Control and Information Sciences 370: 317-329
Journal
Recent Progress in Robotics Viable Robotic Service to Human (Lecture Notes in Control and Information Sciences)
Pages
317-329
Date Issued
2008
Date
2008
Author(s)
Lee, Sukhan
Suh, Il Hong
Kim, Mun Sang
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. © 2008 Springer-Verlag Berlin Heidelberg.
Type
journal article
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