Treffer: Eye-gaze estimation under various head positions and iris states
Title:
Eye-gaze estimation under various head positions and iris states
Authors:
Source:
Expert systems with applications. 42(1):510-518
Publisher Information:
Amsterdam: Elsevier, 2015.
Publication Year:
2015
Physical Description:
print, 1/2 p
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Logiciel, Software, Organisation des mémoires. Traitement des données, Memory organisation. Data processing, Traitement des données. Listes et chaînes de caractères, Data processing. List processing. Character string processing, Intelligence artificielle, Artificial intelligence, Reconnaissance des formes. Traitement numérique des images. Géométrie algorithmique, Pattern recognition. Digital image processing. Computational geometry, Analyse donnée, Data analysis, Análisis datos, Analyse régression, Regression analysis, Análisis regresión, Calcul variationnel, Variational calculus, Cálculo de variaciones, Efficacité, Efficiency, Eficacia, Estimation Bayes, Bayes estimation, Estimación Bayes, Estimation mouvement, Motion estimation, Estimación movimiento, Evaluation performance, Performance evaluation, Evaluación prestación, Fermeture, Closure, Cerradura, Haute résolution, High resolution, Alta resolucion, Mouvement corporel, Body movement, Movimiento corporal, Mouvement oculaire, Eye movement, Movimiento ocular, Regard, Gaze, Mirada, Régression logistique, Logistic regression, Regresión logística, Source lumineuse, Light source, Fuente luminosa, Source lumière, Light sources, Traitement image, Image processing, Procesamiento imagen, Tête, Head, Cabeza, Caméra vidéo, Video cameras, Cámara de vídeo, Interface naturelle, Natural interface, Interfase natural, Résolution image, Image resolution, Resolución imagen, Bayesian logistic regression, Gaze estimation, Kinect
Document Type:
Fachzeitschrift
Article
File Description:
text
Language:
English
Author Affiliations:
Departement d'informatique, Universite de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada
ISSN:
0957-4174
Rights:
Copyright 2015 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Accession Number:
edscal.28843419
Database:
PASCAL Archive
Weitere Informationen
This paper describes a method for eye-gaze estimation under normal head movement. In this method, head position and orientation are acquired by Kinect depth data and eye direction is obtained from high resolution images. We propose the Bayesian multinomial logistic regression based on a variational approximation to construct a gaze mapping function and to verify iris state. Our method eliminates limitation of head movements, eye closure and light source as common drawbacks in most conventional techniques. The efficiency of the proposed method is validated by performance evaluation for multiple people with different distances and poses to the camera under various eye states.