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

57 Citations (Scopus)

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.

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
Pages317-329
Number of pages13
DOIs
Publication statusPublished - 2008
Externally publishedYes

Publication series

NameLecture Notes in Control and Information Sciences
Volume370
ISSN (Print)0170-8643

ASJC Scopus subject areas

  • Library and Information Sciences

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