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)