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QRCP-based preprocessing for illumination invariant measure under severe illumination variations

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Abstract

The previous illumination invariant measure performs unsatisfactorily under severe illumination variations, since severe illumination variations cause large differences of illumination intensities in the face local region, especially for the edge of cast shadows. In this paper, we conduct orthogonal triangular with column pivoting (QRCP) decomposition in the \(N\times N\) local block which generates N QRCP coefficients. QRCP unit is defined as the quotient of the largest QRCP coefficient and the sum of all QRCP coefficients in the local block. To further control the illumination effect, we obtain corrected QRCP unit by calculating the \(\gamma \) power of QRCP coefficients. We define QRCP Image as the combination of corrected QRCP units. Finally, QRCP Image is employed as a preprocessing method of illumination invariant measures (LNN-face/BGIR-face/EGIR-face). The experimental results on the illumination-varying face databases (Extended Yale B and CMU PIE) indicate that the proposed method is efficient to tackle severe illumination variations.

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We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant 61802203, in part by the China Postdoctoral Science Foundation under Grant 2019M651653, in part by the Postdoctoral Research Funding Program of Jiangsu Province under Grant 2019K124, and in part by the Natural Science Foundation of Nanjing University of Posts and Telecommunications under Grant NY221081.

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Authors and Affiliations

  1. College of Automation and College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Wenyuan Road, Nanjing, 210023, Jiangsu, People’s Republic of China

    Li Feng-Yao, Hu Chang-Hui & Liu Yu

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  1. Li Feng-Yao
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  2. Hu Chang-Hui
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  3. Liu Yu
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Correspondence to Hu Chang-Hui.

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Feng-Yao, L., Chang-Hui, H. & Yu, L. QRCP-based preprocessing for illumination invariant measure under severe illumination variations. SIViP 17, 753–760 (2023). https://doi.org/10.1007/s11760-022-02283-3

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