Result: Pruned resampling : Probabilistic model selection schemes for sequential face recognition
Title:
Pruned resampling : Probabilistic model selection schemes for sequential face recognition
Authors:
Source:
IEICE transactions on information and systems. 90(8):1151-1159
Publisher Information:
Oxford: Oxford University Press, 2007.
Publication Year:
2007
Physical Description:
print, 11 ref
Original Material:
INIST-CNRS
Subject Terms:
Electronics, Electronique, Computer science, Informatique, Telecommunications, Télécommunications, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Intelligence artificielle, Artificial intelligence, Reconnaissance des formes. Traitement numérique des images. Géométrie algorithmique, Pattern recognition. Digital image processing. Computational geometry, Telecommunications et theorie de l'information, Telecommunications and information theory, Théorie de l'information, du signal et des communications, Information, signal and communications theory, Traitement du signal, Signal processing, Reconnaissance des formes, Pattern recognition, Traitement des images, Image processing, Algorithme apprentissage, Learning algorithm, Algoritmo aprendizaje, Approche probabiliste, Probabilistic approach, Enfoque probabilista, Biométrie, Biometrics, Biometría, Etude comparative, Comparative study, Estudio comparativo, Evaluation performance, Performance evaluation, Evaluación prestación, Loi a posteriori, Posterior distribution, Ley a posteriori, Modèle probabiliste, Probabilistic model, Modelo probabilista, Méthode Monte Carlo, Monte Carlo method, Método Monte Carlo, Méthode rééchantillonnage, Resampling method, Reconnaissance automatique, Automatic recognition, Reconocimiento automático, Reconnaissance forme, Pattern recognition, Reconocimiento patrón, Reconnaissance visage, Face recognition, Simulation numérique, Numerical simulation, Simulación numérica, Système en ligne, On-line systems, Sélection modèle, Model selection, Selección modelo, Taux erreur, Error rate, Indice error, Temps traitement, Processing time, Tiempo proceso, Traitement image, Image processing, Procesamiento imagen, Sequential Monte Carlo, face recognition, model comparison, pruning, resampling
Document Type:
Conference
Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Science and Technical Research Laboratories, NHK (Japan Broadcasting Corporation), Tokyo, 157-8510, Japan
Faculty of Science and Engineering, Waseda University, Tokyo, 169-8555, Japan
Faculty of Science and Engineering, Waseda University, Tokyo, 169-8555, Japan
ISSN:
0916-8532
Rights:
Copyright 2007 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
Telecommunications and information theory
Telecommunications and information theory
Accession Number:
edscal.18993463
Database:
PASCAL Archive
Further Information
This paper proposes probabilistic pruning techniques for a Bayesian video face recognition system. The system selects the most probable face model using model posterior distributions, which can be calculated using a Sequential Monte Carlo (SMC) method. A combination of two new pruning schemes at the resampling stage significantly boosts computational efficiency by comparison with the original online learning algorithm. Experimental results demonstrate that this approach achieves better performance in terms of both processing time and ID error rate than a contrasting approach with a temporal decay scheme.