Supervised Transform Learning for Face Recognition
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  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.
KeywordsSupervised sparsifying transform Sparse representation Dictionary learning Face recognition
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- 4.Child, D.: The Essentials of Factor Analysis. Bloomsbury Academic (2006)Google Scholar
- 5.Cover, T.M., Thomas, J.A.: Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing). Wiley-Interscience (2006)Google Scholar
- 9.Jolliffe, I.: Principal Component Analysis. Springer Series in Statistics. Springer (2002)Google Scholar
- 10.Kostadinov, D., Voloshynovskiy, S., Diephuis, M.: Visual information encoding for face recognition: sparse coding vs vector quantization. In: 4th Joint WIC IEEE Symposium on Information Theory and Signal Processing in the Benelux, Eindhoven, Netherlands, vol. 35 (May 2014)Google Scholar
- 11.Lay, D.C.: Linear Algebra and Its Applications, 4th edn. Addison-Wesley (2006)Google Scholar
- 15.Ravishankar, S., Bresler, Y.: ℓ0 sparsifying transform learning with efficient optimal updates and convergence guarantees. CoRR abs/1501.02859 (2015)Google Scholar
- 16.Bracewell, R.N.: The Fourier Transform and Its Applications. Electrical engineering series. McGraw Hill (2000)Google Scholar
- 17.Stphane, M.: A Wavelet Tour of Signal Processing: The Sparse Way, 3rd edn. Academic Press (2008)Google Scholar