Bayesian Network Enhanced Prediction for Multiple Facial Feature Tracking
Huang, Li (Zhejiang University (Hangzhou, Xina). College of Computer Science)
Su, Congyong (Zhejiang University (Hangzhou, Xina). College of Computer Science)

Fecha:	2005
Resumen:	It is challenging to track multiple facial features simultaneously in video while rich facial expressions are presented in a human face. To accurately predict the positions of multiple facial features' contours is important and difficult. This paper proposes a multi-cue prediction model based tracking algorithm. In the prediction model, CAMSHIFT is used to track the face in video in advance, and facial features' spatial constraint is utilized to roughly obtain the positions of facial features. Second order autoregressive process (ARP) based dynamic model is combined with graphical model (Bayesian network) based dynamic model. Incorporating ARP's quickness into graphical model's accurateness, we obtain the fusion of the prediction. Finally the prediction model and the measurement model are integrated into the framework of Kalman filter. The experimental results show that our algorithm can accurately track multiple facial features with varied facial expressions.
Derechos:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.
Lengua:	Anglès
Documento:	Article ; recerca ; Versió publicada
Materia:	Mostra de les múltiples característiques facials ; Xarxa fiable ; Model gràfic ; Muestra de las múltiples características faciales ; Red fiable ; Modelo gráfico ; Multiple Facial Feature Tracking ; Bayesian Network ; Graphical Model ; CAMSHIFT
Publicado en:	ELCVIA : Electronic Letters on Computer Vision and Image Analysis, V. 5 n. 3 (2005) p. 157-169, ISSN 1577-5097

Adreça original: https://elcvia.cvc.uab.es/article/view/v5-n3-huang-su
Adreça alternativa: https://raco.cat/index.php/ELCVIA/article/view/31630
DOI: 10.5565/rev/elcvia.114

13 p, 1.8 MB

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