Nourbakhsh, Farshad, Granger, Eric and Fumera, Giorgio.
2017.
« An extended sparse classification framework for domain adaptation in video surveillance ».
In Computer Vision – ACCV 2016 Workshops : ACCV 2016 International Workshops, Taipei, Taiwan, November 20-24, 2016, Revised Selected Papers, Part III (Taipei, Taiwan, Nov. 20-24, 2016)
Coll. « Lecture Notes in Computer Science », vol. 10118.
pp. 360-376.
Springer Verlag.
Compte des citations dans Scopus : 1.
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Abstract
Still-to-video face recognition (FR) systems used in video surveillance applications capture facial trajectories across a network of distributed video cameras and compare them against stored distributed facial models. Currently, the performance of state-of-the-art systems is severely affected by changes in facial appearance caused by variations in, e.g., pose, illumination and scale in different camera viewpoints. More- over, since an individual is typically enrolled using one or few reference stills captured during enrolment, face models are not robust to intra-class variation. In this paper, the Extended Sparse Representation Classiffication through Domain Adaptation (ESRC-DA) algorithm is proposed to improve performance of still-to-video FR. The system's facial mod- els are thereby enhanced by integrating variational information from its operational domain. In particular, robustness to intra-class variations is improved by exploiting: (1) an under-sampled dictionary from tar- get reference facial stills captured under controlled conditions; and (2) an auxiliary dictionary from an abundance of unlabelled facial trajecto- ries captured under different conditions, from each camera viewpoint in the surveillance network. Accuracy and effciency of the proposed tech- nique is compared to state-of-the-art still-to-video FR techniques using videos from the Chokepoint and COX-S2V databases. Results indicate that ESRC-DA with dictionary learning of unlabelled trajectories provides the highest level of accuracy, while maintaining a low complexity.
| Item Type: | Conference proceeding |
|---|---|
| ISBN: | 03029743 |
| Professor: | Professor Granger, Éric |
| Affiliation: | Génie de la production automatisée |
| Date Deposited: | 11 Apr 2017 14:19 |
| Last Modified: | 28 Jan 2020 16:17 |
| URI: | https://espace2.etsmtl.ca/id/eprint/15044 |
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