Hand posture recognition using adaboost with SIFT for human robot interaction

Chieh Chih Wang*, Ko Chih Wang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

61 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationRecent Progress in Robotics
Subtitle of host publicationViable Robotic Service to Human
EditorsSukhan Lee, Il Hong Suh, Kim Mun Sang
Number of pages13
Publication statusPublished - 2008
Externally publishedYes

Publication series

NameLecture Notes in Control and Information Sciences
ISSN (Print)0170-8643

ASJC Scopus subject areas

  • Library and Information Sciences


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