A compact discriminative representation for efficient image-set classification with application to biometric recognition
Date
2013
Authors
Uzair, M.
Mahmood, A.
Mian, A.
McDonald, C.
Editors
Advisors
Journal Title
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Volume Title
Type:
Conference paper
Citation
Proceedings - 2013 International Conference on Biometrics, ICB 2013, 2013, pp.1-8
Statement of Responsibility
Conference Name
6th IAPR International Conference on Biometrics, ICB 2013 (4 Jun 2013 - 7 Jun 2013 : Madrid, Spain)
Abstract
We present a simple yet compact and discriminative representation for image sets which can efficiently be used for image-set based object classification. For each image-set we compute a global covariance matrix which captures correlated variations in all image-set dimensions. Without loss of information, we compact the covariance matrix into a lower triangular matrix by using Cholesky decomposition. While preserving discrimination capability of the representation, we obtain further compression by applying Multiple Discriminant Analysis. As a result, we are able to represent image sets containing N samples each of dimensionality d by a single vector whose dimensionality is << N d. We apply the proposed representation to various biometric applications such as image-set based face recognition and person identification using image-sets of periocular regions. To show that our representation is generic, we also report results for image-set based object categorization. We observe improved accuracy and significant speedup over the current state-of-the-art techniques on standard datasets.
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Dissertation Note
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Link to a related website: https://api.research-repository.uwa.edu.au/files/4456799/2363180_combined.pdf, Open Access via Unpaywall
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Copyright 2013 IEEE