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Linear Regression Correlation Filter: An Application to Face Recognition

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1022))

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Abstract

This paper proposes a novel method of designing a correlation filter for frequency domain pattern recognition. The proposed correlation filter is designed with linear regression technique and termed as linear regression correlation filter. The design methodology of linear regression correlation filter is completely different from standard correlation filter design techniques. The proposed linear regression correlation filter is estimated or predicted from a linear subspace of weak classifiers. The proposed filter is evaluated on standard benchmark database and promising results are reported.

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Notes

  1. 1.

    thr: hard threshold selected empirically.

  2. 2.

    http://vision.ucsd.edu/~leekc/ExtYaleDatabase/ExtYaleB.html.

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Correspondence to Tiash Ghosh .

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Ghosh, T., Banerjee, P.K. (2020). Linear Regression Correlation Filter: An Application to Face Recognition. In: Chaudhuri, B., Nakagawa, M., Khanna, P., Kumar, S. (eds) Proceedings of 3rd International Conference on Computer Vision and Image Processing. Advances in Intelligent Systems and Computing, vol 1022. Springer, Singapore. https://doi.org/10.1007/978-981-32-9088-4_32

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  • DOI: https://doi.org/10.1007/978-981-32-9088-4_32

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