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Learning of graph compressed dictionaries for sparse representation classification


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Nourbakhsh, Farshad et Granger, Eric. 2016. « Learning of graph compressed dictionaries for sparse representation classification ». In Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2016) (Rome, Italy, Feb. 24-26, 2016), p. 309-316. SciTePress.
Compte des citations dans Scopus : 2.


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Despite the limited target data available to design face models in video surveillance applications, many faces of non-target individuals may be captured in operational environments, and over multiple cameras, 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 accuracy with compressed dictionaries because it exploits structural information to represent intra-class variations. Graph factorization compression has been shown to efficiently compress data, and can therefore rapidly construct compressed dictionaries. Accuracy and efficiency of the proposed technique is assessed and compared to reference sparse coding and dictionary learning technique using videos 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.

Type de document: Compte rendu de conférence
Granger, Éric
Affiliation: Génie de la production automatisée
Date de dépôt: 21 juin 2016 15:03
Dernière modification: 17 août 2016 19:08
URI: http://espace2.etsmtl.ca/id/eprint/12842

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