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Authors: C. Pagano 1 ; E. Granger 1 ; R. Sabourin 1 ; G. L. Marcialis 2 and F. Roli 2

Affiliations: 1 Université du Québec, Canada ; 2 University of Cagliari, Italy

Keyword(s): Multi-classifier Systems, Incremental Learning, Adaptive Biometrics, Change Detection, Face Recognition, Video Surveillance.

Related Ontology Subjects/Areas/Topics: Applications ; Biomedical Engineering ; Biomedical Signal Processing ; Biometrics ; Biometrics and Pattern Recognition ; Ensemble Methods ; Incremental Learning ; Multimedia ; Multimedia Signal Processing ; Object Recognition ; Pattern Recognition ; Software Engineering ; Telecommunications ; Theory and Methods

Abstract: Person re-identification from facial captures remains a challenging problem in video surveillance, in large part due to variations in capture conditions over time. The facial model of a target individual is typically designed during an enrolment phase, using a limited number of reference samples, and may be adapted as new reference videos become available. However incremental learning of classifiers in changing capture conditions may lead to knowledge corruption. This paper presents an active framework for an adaptive multi-classifier system for video-to-video face recognition in changing surveillance environments. To estimate a facial model during the enrolment of an individual, facial captures extracted from a reference video are employed to train an individual-specific incremental classifier. To sustain a high level of performance over time, a facial model is adapted in response to new reference videos according the type of concept change. If the system detects that the facial cap tures of an individual incorporate a gradual pattern of change, the corresponding classifier(s) are adapted through incremental learning. In contrast, to avoid knowledge corruption, if an abrupt pattern of change is detected, a new classifier is trained on the new video data, and combined with the individual’s previously-trained classifiers. For validation, a specific implementation is proposed, with ARTMAP classifiers updated using an incremental learning strategy based on Particle Swarm Optimization, and the Hellinger Drift Detection Method is used for change detection. Simulation results produced with Faces in Action video data indicate that the proposed system allows for scalable architectures that maintains a significantly higher level of accuracy over time than a reference passive system and an adaptive Transduction Confidence Machine-kNN classifier, while controlling computational complexity. (More)

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Paper citation in several formats:
Pagano, C.; Granger, E.; Sabourin, R.; Marcialis, G. and Roli, F. (2015). Adaptive Classification for Person Re-identification Driven by Change Detection. In Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM; ISBN 978-989-758-076-5; ISSN 2184-4313, SciTePress, pages 45-55. DOI: 10.5220/0005184700450055

@conference{icpram15,
author={C. Pagano. and E. Granger. and R. Sabourin. and G. L. Marcialis. and F. Roli.},
title={Adaptive Classification for Person Re-identification Driven by Change Detection},
booktitle={Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM},
year={2015},
pages={45-55},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005184700450055},
isbn={978-989-758-076-5},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM
TI - Adaptive Classification for Person Re-identification Driven by Change Detection
SN - 978-989-758-076-5
IS - 2184-4313
AU - Pagano, C.
AU - Granger, E.
AU - Sabourin, R.
AU - Marcialis, G.
AU - Roli, F.
PY - 2015
SP - 45
EP - 55
DO - 10.5220/0005184700450055
PB - SciTePress