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Authors: Farshad Nourbakhsh and Eric Granger

Affiliation: Université du Québec, Canada

ISBN: 978-989-758-173-1

Keyword(s): Matrix Factorization, Graph Compression, Dictionary Learning, Sparse Representation Classification, Face Recognition, Video Surveillance.

Related Ontology Subjects/Areas/Topics: Applications ; Biomedical Engineering ; Biomedical Signal Processing ; Biometrics ; Biometrics and Pattern Recognition ; Classification ; Clustering ; Computer Vision, Visualization and Computer Graphics ; Image and Video Analysis ; Image Understanding ; Matrix Factorization ; Multimedia ; Multimedia Signal Processing ; Object Recognition ; Pattern Recognition ; Software Engineering ; Sparsity ; Telecommunications ; Theory and Methods ; Video Analysis

Abstract: Despite the limited target data available to design face models in video surveillance applications, many faces of non-target individuals may be captured over multiple cameras in operational environments to improve robustness to variations. This paper focuses on Sparse Representation Classification (SRC) techniques that are suitable for the design of still-to-video FR systems based on under-sampled dictionaries. The limited reference data available during enrolment is complemented by an over-complete external dictionary that is formed with an abundance of faces from non-target individuals. In this paper, the Graph-Compressed Dictionary Learning (GCDL) technique is proposed to learn compact auxiliary dictionaries for SRC. GCDL is based on matrix factorization, and allows to maintain a high level of SRC accuracy with compressed dictionaries because it exploits structural information to represent intra-class variations. Graph compression based on matrix factorization shown to efficiently compress data, and can therefore rapidly construct compact dictionaries. Accuracy and efficiency of the proposed GCDL technique is assessed and compared to reference sparse coding and dictionary learning techniques using images from the CAS-PEAL database. GCDL is shown to provide fast matching and adaptation of compressed dictionaries to new reference faces from the video surveillance environments. (More)

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Paper citation in several formats:
Nourbakhsh F. and Granger E. (2016). Learning of Graph Compressed Dictionaries for Sparse Representation Classification.In Proceedings of the 5th International Conference on Pattern Recognition Applications and MethodsISBN 978-989-758-173-1, pages 309-316. DOI: 10.5220/0005710403090316

@conference{icpram16,
author={Farshad Nourbakhsh and Eric Granger},
title={Learning of Graph Compressed Dictionaries for Sparse Representation Classification},
booktitle={Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods},
year={2016},
pages={309-316},
doi={10.5220/0005710403090316},
isbn={978-989-758-173-1},
}

TY - CONF

JO - Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods
TI - Learning of Graph Compressed Dictionaries for Sparse Representation Classification
SN - 978-989-758-173-1
AU - Nourbakhsh F.
AU - Granger E.
PY - 2016
SP - 309
EP - 316
DO - 10.5220/0005710403090316

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