Hand posture recognition using hidden conditional random fields

Te Cheng Liu*, Ko Chih Wang, Augustine Tsai, Chieh-Chih Wang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

Abstract

Body-language understanding is essential to human robot interaction, and hand posture recognition is one of the most important components in a body-language recognition system. The existing hand posture recognition approaches based on robust local features such as SIFT can be invariant to background noise and in-plane rotation. However the ignorance of the relationships among local features is a fundamental issue. The part-based models argue that objects of the same category share the same part-structure which consists of parts and relationships among parts. In this paper, a discriminative partbased model, Hidden Conditional Random Fields (HCRFs), is used to recognize hand postures. Although the existing global locations of features have been used to consider large scale dependency among parts in the HCRFs framework, the results are not invariant to in-plane rotation. New features by the distance to the image center are proposed to encode the global relationship as well as to perform in-plane rotationinvariant recognition. The experimental results demonstrate that the proposed approach is in-plane rotation-invariant and out performs the approach using Ada Boost with SIFT.

Original languageEnglish
Title of host publication2009 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2009
Pages1828-1833
Number of pages6
DOIs
StatePublished - 4 Nov 2009
Event2009 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2009 - Singapore, Singapore
Duration: 14 Jul 200917 Jul 2009

Publication series

NameIEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM

Conference

Conference2009 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2009
CountrySingapore
CitySingapore
Period14/07/0917/07/09

Fingerprint Dive into the research topics of 'Hand posture recognition using hidden conditional random fields'. Together they form a unique fingerprint.

Cite this