Abstract
This paper proposes a diagonal symmetric pattern (DSP) to develop the illumination invariant measure for severe illumination variations. Firstly, the subtraction of two diagonal symmetric pixels is defined as the DSP unit in the face local region, which may be positive or negative. The DSP model is obtained by combining the positive and negative DSP units. Then, the DSP model can be used to generate several DSP images based on the 4 × 4 block region by controlling the proportions of positive and negative DSP units, which results in the DSP image. The single DSP image with the arctangent function can develop the DSP-face. Multi DSP images employ the extended sparse representation classification (ESRC) as the classifier that can form the DSP images based classification (DSPC). Further, the DSP model is integrated with the pre-trained deep learning (PDL) model to construct the DSP-PDL model. Finally, the experimental results on the Extended Yale B, CMU PIE and VGGFace2 test face databases indicate that the proposed methods are efficient to tackle severe illumination variations.
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References
Hu, C., Lu, X., Liu, P., Jing, X., Yue, D.: Single sample face recognition under varying illumination via QRCP decomposition. IEEE Trans. Image Process. 28(5), 2624–2638 (2019)
Hu, C., Zhang, Y., Wu, F., Lu, X., Liu, P., Jing, X.: Toward driver face recognition in the intelligent traffic monitoring systems. IEEE Trans. Intell. Transp. Syst. (to be published). https://doi.org/10.1109/tits.2019.2945923
Horn, B.: Robot Vision. MIT Press, Cambridge (1997)
Fu, X., Zeng, D., Huang, Y., Zhang, X., Ding, X.: A weighted variational model for simultaneous reflectance and illumination estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, pp. 2782–2790. IEEE (2016)
Hu, C., Lu, X., Ye, M., Zeng, W.: Singular value decomposition and local near neighbors for face recognition under varying illumination. Pattern Recogn. 64, 60–83 (2017)
Wang, B., Li, W., Yang, W., Liao, Q.: Illumination normalization based on weber’s law with application to face recognition. IEEE Signal Process. Lett. 18(8), 462–465 (2011)
Lai, Z., Dai, D., Ren, C., Huang, K.: Multiscale logarithm difference edgemaps for face recognition against varying lighting conditions. IEEE Trans. Image Process. 24(6), 1735–1747 (2015)
Heikkilä, M., Pietikäinen, M., Schmid, C.: Description of interest regions with local binary patterns. Pattern Recogn. 42(3), 425–436 (2009)
Chakraborty, S., Singh, S., Chakraborty, P.: Centre symmetric quadruple pattern: a novel descriptor for facial image recognition and retrieval. Pattern Recogn. Lett. 115, 50–58 (2018)
Parkhi, O., Vedaldi, A., Zisserman, A.: Deep face recognition. In: Proceedings of the British Machine Vision Conference, Swansea, pp. 1–12, BMVA Press (2015)
Deng, J., Guo, J., Xue, N., Zafeiriou, S.: Arcface: additive angular margin loss for deep face recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, pp. 4690–4699. IEEE (2019)
Deng, W., Hu, J., Guo, J.: Extended SRC: undersampled face recognition via intraclass variant dictionary. IEEE Trans. Pattern Anal. Mach. Intell. 34(9), 1864–1870 (2012)
Georghiades, A., Belhumeur, P., Kriegman, D.: From few to many: illumination cone models for face recognition under variable lighting and pose. IEEE Trans. Pattern Anal. Mach. Intell. 23(6), 643–660 (2001)
Sim, T., Baker, S., Bsat, M.: The CMU pose, illumination, and expression database. IEEE Trans. Pattern Anal. Mach. Intell. 25(12), 504–507 (2003)
Cao, Q., Shen, L., Xie, W., Parkhi, O.M., Zisserman, A.: VGGFace2: a dataset for recognising faces across pose and age. In: Proceedings of the IEEE Conference on Automatic Face and Gesture Recognition, Xi’an, pp. 67–74, IEEE (2018)
Acknowledgments
This work was supported in part by National Natural Science Foundation of China (No. 61802203), in part by National Science Foundation of Jiangsu Province (No. BK20180761), in part by China Postdoctoral Science Foundation (No. 2019M651653), in part by Postdoctoral Research Funding Program of Jiangsu Province (No. 2019K124), and in part by NUPTSF (No. NY218119).
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Hu, C., Ye, M., Zhang, Y., Lu, X. (2020). Diagonal Symmetric Pattern Based Illumination Invariant Measure for Severe Illumination Variations. In: Peng, Y., et al. Pattern Recognition and Computer Vision. PRCV 2020. Lecture Notes in Computer Science(), vol 12306. Springer, Cham. https://doi.org/10.1007/978-3-030-60639-8_3
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DOI: https://doi.org/10.1007/978-3-030-60639-8_3
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