Authors:
Saman Bashbaghi
1
;
Eric Granger
1
;
Robert Sabourin
1
and
Guillaume-Alexandre Bilodeau
2
Affiliations:
1
Université du Québec, Canada
;
2
Polytechnique Montréal, Canada
Keyword(s):
Face Recognition, Video Surveillance, Multi-classifier System, Single Sample Per Person, Random Subspace Method, Domain Adaptation, Dynamic Classifier Selection.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Biometrics
;
Biometrics and Pattern Recognition
;
Classification
;
Ensemble Methods
;
Feature Selection and Extraction
;
Multiclassifier Fusion
;
Multimedia
;
Multimedia Signal Processing
;
Object Recognition
;
Pattern Recognition
;
Software Engineering
;
Telecommunications
;
Theory and Methods
Abstract:
Still-to-video face recognition (FR) plays an important role in video surveillance, allowing to recognize individuals
of interest over a network of video cameras. Watch-list screening is a challenging video surveillance
application, because faces captured during enrollment (with still camera) may differ significantly from those
captured during operations (with surveillance cameras) under uncontrolled capture conditions (with variations
in, e.g., pose, scale, illumination, occlusion, and blur). Moreover, the facial models used for matching are
typically designed a priori with a limited number of reference stills. In this paper, a multi-classifier system
is proposed that exploits domain adaptation and multiple representations of face captures. An individual-specific
ensemble of exemplar-SVM (e-SVM) classifiers is designed to model the single reference still of each
target individual, where different random subspaces, patches, and face descriptors are employed to generate
a diverse pool
of classifiers. To improve robustness of face models, e-SVMs are trained using the limited
number of labeled faces in reference stills from the enrollment domain, and an abundance of unlabeled faces
in calibration videos from the operational domain. Given the availability of a single reference target still, a
specialized distance-based criteria is proposed based on properties of e-SVMs for dynamic selection of the
most competent classifiers per probe face. The proposed approach has been compared to reference systems
for still-to-video FR on videos from the COX-S2V dataset. Results indicate that ensemble of e-SVMs designed
using calibration videos for domain adaptation and dynamic ensemble selection yields a high level of FR
accuracy and computational efficiency.
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