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Discriminant Non-negative Matrix Factorization and Projected Gradients for Frontal Face Verification

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Biometrics and Identity Management (BioID 2008)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5372))

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Abstract

A novel Discriminant Non-negative Matrix Factorization (DNMF) method that uses projected gradients, is presented in this paper. The proposed algorithm guarantees the algorithm’s convergence to a stationary point, contrary to the methods introduced so far, that only ensure the non-increasing behavior of the algorithm’s cost function. The proposed algorithm employs some extra modifications that make the method more suitable for classification tasks. The usefulness of the proposed technique to the frontal face verification problem is also demonstrated.

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© 2008 Springer-Verlag Berlin Heidelberg

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Kotsia, I., Zafeiriou, S., Pitas, I. (2008). Discriminant Non-negative Matrix Factorization and Projected Gradients for Frontal Face Verification. In: Schouten, B., Juul, N.C., Drygajlo, A., Tistarelli, M. (eds) Biometrics and Identity Management. BioID 2008. Lecture Notes in Computer Science, vol 5372. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89991-4_9

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  • DOI: https://doi.org/10.1007/978-3-540-89991-4_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-89990-7

  • Online ISBN: 978-3-540-89991-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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