Robust head gestures recognition for assistive technology

Juan R. Terven, Joaquin Salas, Bogdan Raducanu

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

17 Citas (Scopus)

Resumen

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.

Idioma originalInglés
Título de la publicación alojadaPattern Recognition - 6th Mexican Conference, MCPR 2014, Proceedings
EditorialSpringer Verlag
Páginas152-161
Número de páginas10
ISBN (versión impresa)9783319074900
DOI
EstadoPublicada - 2014
Evento6th Mexican Conference on Pattern Recognition, MCPR 2014 - Cancun, México
Duración: 25 jun. 201428 jun. 2014

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen8495 LNCS
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conferencia

Conferencia6th Mexican Conference on Pattern Recognition, MCPR 2014
País/TerritorioMéxico
CiudadCancun
Período25/06/1428/06/14

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