Robust head gestures recognition for assistive technology

Juan R. Terven, Joaquin Salas, Bogdan Raducanu

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

17 Scopus citations

Abstract

This paper presents a system capable of recognizing six head gestures: nodding, shaking, turning right, turning left, looking up, and looking down. The main difference of our system compared to other methods is that the Hidden Markov Models presented in this paper, are fully connected and consider all possible states in any given order, providing the following advantages to the system: (1) allows unconstrained movement of the head and (2) it can be easily integrated into a wearable device (e.g. glasses, neck-hung devices), in which case it can robustly recognize gestures in the presence of ego-motion. Experimental results show that this approach outperforms common methods that use restricted HMMs for each gesture.

Original languageEnglish
Title of host publicationPattern Recognition - 6th Mexican Conference, MCPR 2014, Proceedings
PublisherSpringer Verlag
Pages152-161
Number of pages10
ISBN (Print)9783319074900
DOIs
StatePublished - 2014
Event6th Mexican Conference on Pattern Recognition, MCPR 2014 - Cancun, Mexico
Duration: 25 Jun 201428 Jun 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8495 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference6th Mexican Conference on Pattern Recognition, MCPR 2014
Country/TerritoryMexico
CityCancun
Period25/06/1428/06/14

Keywords

  • assistive technology
  • head gestures recognition
  • hidden markov models
  • social interactions

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