Abstract
In this paper we investigate transform learning and apply it to face recognition problem. The focus is to find a transformation matrix that transforms the signal into a robust to noise, discriminative and compact representation. We propose a method that finds an optimal transform under the above constrains. The non-sparse variant of the presented method has a closed form solution whereas the sparse one may be formulated as a solution to a sparsity regularized problem. In addition we give a generalized version of the proposed problem and we propose a prior on the data distribution across the dimensions in the transform domain.
Supervised transform learning is applied to the MVQ [10] method and is tested on a face recognition application using the YALE B database. The recognition rate and the robustness to noise is superior compared to the original MVQ based on k-means.
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Kostadinov, D., Voloshynovskiy, S., Ferdowsi, S., Diephuis, M., Scherer, R. (2015). Supervised Transform Learning for Face Recognition. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2015. Lecture Notes in Computer Science(), vol 9119. Springer, Cham. https://doi.org/10.1007/978-3-319-19324-3_66
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DOI: https://doi.org/10.1007/978-3-319-19324-3_66
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-19323-6
Online ISBN: 978-3-319-19324-3
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