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Face recognition via selective denoising, filter faces and hog features

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Abstract

Face recognition has become a very important topic in recent years. In this paper, we introduce selective denoising with block matching and 3D filtering (BM3D) or video BM3D (VBM3D), compute filter faces, and extract the histogram of oriented gradient (HOG) features from the extracted feature maps. We apply our new method to both illumination invariant face recognition and hyperspectral face recognition. For illumination invariant face recognition, our proposed method in this paper achieves the highest correct classification rate (98.4%) for the Extended Yale Face dataset B and similar results (100%) for the CMU-PIE dataset. For hyperspectral face recognition, our new method achieves perfect classification rate (100%) for both the PolyU-HSFD dataset and the CMU-HSFD dataset.

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Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

References

  1. Xie, X., Zheng, W.S., Lai, J., Yuen, P.C., Suen, C.Y.: Normalization of face illumination based on large-and small-scale features. IEEE Trans. Image Process. 20(7), 1807–1821 (2011)

    MathSciNet  Google Scholar 

  2. Zhang, T., Tang, Y.Y., Fang, B., Shang, Z., Liu, X.: Face recognition under varying illumination using gradientfaces. IEEE Trans. Image Process. 18(11), 2599–2606 (2009)

    MathSciNet  Google Scholar 

  3. 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)

    Google Scholar 

  4. Chen, W., Er, M., Wu, S.: Illumination compensation and normalization for robust face recognition using discrete cosine transform in logarithm domain. IEEE Trans. Syst., Man Cybern. B, Cybern. 36, 458–466 (2006)

    Google Scholar 

  5. Chen, T., Yin, W., Zhou, X.S., Comaniciu, D., Huang, T.S.: Total variation models for variable lighting face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 28(9), 1519–1524 (2006)

    Google Scholar 

  6. Du, S., Ward, R.K.: Adaptive region-based image enhancement method for robust face recognition under variable illumination conditions. IEEE Trans. Circuits Syst. Video Technol. 20(9), 1165–1175 (2010)

    Google Scholar 

  7. Lai, Z.R., Dai, D.Q., Ren, C.X., Huang, K.K.: Multiscale logarithm difference edgemaps for face recognition against varying lighting conditions. IEEE Trans. Image Process. 24(6), 1735–1747 (2015)

    MathSciNet  Google Scholar 

  8. Wang, H., Li S., Wang, Y.: Face recognition under varying lighting conditions using self-quotient image. In: Proc. Sixth IEEE Int. Conf. on Automatic Face and Gesture Recognition, Seoul, Korea, pp. 819–824 (2004).

  9. Hu, H.: Illumination invariant face recognition based on dual-tree complex wavelet transform. IET Comput. Vision 9(2), 163–173 (2015)

    Google Scholar 

  10. Nikan, S., Ahmadi, M.: Local gradient-based illumination invariant face recognition using local phase quantisation and multi-resolution local binary pattern fusion. IET Image Proc. 9(1), 12–21 (2015)

    Google Scholar 

  11. Faraji, M.R., Qi, J.: Face recognition under varying illumination based on adaptive homomorphic eight local directional patterns. IET Comput. Vision 9(3), 390–399 (2015)

    Google Scholar 

  12. Chen, G.Y., Bui, T.D., Krzyzak, A.: Filter-based face recognition under varying illumination. IET Biometrics 7(6), 628–635 (2018)

    Google Scholar 

  13. Chen, G.Y.: An experimental study for the effects of noise on face recognition algorithms under varying illumination. Multimed. Tools Appl. 78(18), 26615–26631 (2019)

    Google Scholar 

  14. Chen, G.Y., Bui, T.D., Krzyzak, A.: Illumination invariant face recognition using dual-tree complex wavelet transform in logarithm domain. J. Electr. Eng. 70(2), 113–121 (2019)

    Google Scholar 

  15. Tan, X., Triggs, B.: Enhanced local texture sets for face recognition under difficult lighting conditions. IEEE Trans. Image Process. 19(6), 1635–1650 (2010)

    MathSciNet  Google Scholar 

  16. Park, Y.K., Park, S.L., Kim, J.K.: Retinex method based on adaptive smoothing for illumination invariant face recognition. Signal Process. 88(8), 1929–1945 (2008)

    Google Scholar 

  17. Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)

    Google Scholar 

  18. Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: Image denoising by sparse 3D transform-domain collaborative filtering. IEEE Trans. Image Process. 16(8), 2080–2095 (2007)

    MathSciNet  Google Scholar 

  19. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. 1, 886–893 (2005)

    Google Scholar 

  20. Di, W., Zhang, L., Zhang, D., Pan, Q.: Studies on hyperspectral face recognition in visible spectrum with feature band selection. IEEE Trans. Syst. Man Cyber. A: Syst. Humans 40(6), 1354–1361 (2010)

    Google Scholar 

  21. PolyU-HSFD. [Online]. Available: http://www4.comp.polyu.edu.hk/~biometrics/, accessed July 29, 2015.

  22. Pan, Z., Healey, G., Prasad, M., Tromberg, B.: Face recognition in hyperspectral images. IEEE Trans. Pattern Anal. Mach. Intell. 25(12), 1552–1560 (2003)

    Google Scholar 

  23. Denes, L. J., Metes, P., Liu, Y.: Tech. Report, CMU-RI-TR-02–25, Robotics Institute, Carnegie Mellon University, (2002).

  24. Shen, L., Zheng, S.: Hyperspectral face recognition using 3D Gabor wavelets,” International Conference on Pattern Recognition, pp. 1574–157, (2012).

  25. Uzair, M., Mahmood, A., Mian, A.: Hyperspectral face recognition with spatiospectral information fusion and PLS regression. IEEE Trans. Image Process. 24(3), 1127–1137 (2015)

    MathSciNet  Google Scholar 

  26. Chen, G.Y., Sun, W., Xie, W.F.: Hyperspectral face recognition using log-polar Fourier features and collaborative representation-based voting classifiers. IET Biometrics 6(1), 36–42 (2017)

    Google Scholar 

  27. Andersson, F.: Fast inversion of the Radon transform using log-polar coordinates and partial back-projections. SIAM J. Appl. Math. 65, 818–837 (2005)

    MathSciNet  Google Scholar 

  28. Sun, S., Zhao, H., Jin, B.: Robust tensor preserving projection for multispectral face recognition. Math. Problems Eng. 2014, 597245 (2014)

    Google Scholar 

  29. Wang, H., Bau, T.C., Healey, G.: Expression-invariant face recognition in hyperspectral images. Opt. Eng. 53(10), 103102 (2014)

    Google Scholar 

  30. Pan, Z., Healey, G., Tromberg, B.: Hyperspectral face recognition under unknown illumination. Opt. Eng. 46(7), 077201 (2007)

    Google Scholar 

  31. Sharma, V., Gool, L. V.: Image-level classification in hyperspectral images using feature descriptors, with application to face recognition,” Submitted to Computer Vision and Pattern Recognition, (2016). Available at http://arxiv.org/pdf/1605.03428.pdf.

  32. Vinayak, B., Payal, M.: Hyperspectral face recognition using multidimensional clustering on hyperspectral face images, LAP Lambert Academic Publishing, (2014).

  33. Chen, G.Y., Xie, W.F.: Hyperspectral face recognition with minimum noise fraction, histogram of oriented gradient features and collaborative representation-based classifier. J. Intell. Fuzzy Syst. 37, 635–643 (2019)

    Google Scholar 

  34. Chen, G.Y., Krzyzak, A., Xie, W.F.: Hyperspectral face recognition with histogram of oriented gradient features and collaborate representation-based classifier. Multimed. Tools Appl. 81(2), 2299–2310 (2022)

    Google Scholar 

  35. Horn, B.K.P.: Robot Vision, Cambridge. MIT Press, MA (1997)

    Google Scholar 

  36. Donoho, D.L., Johnstone, I.M.: Ideal spatial adaptation by wavelet shrinkage. Biometrika 81(3), 425–455 (1994)

    MathSciNet  Google Scholar 

  37. Green, A.A., Berman, M., Switzer, P., Craig, M.D.: A transformation for ordering multispectral data in terms of image quality with implications for noise removal. IEEE Trans. Geosci. Remote Sens. 26(1), 65–74 (1988)

    Google Scholar 

  38. Lee, J.B., Woodyatt, A.S., Berman, M.: Enhancement of high spectral resolution remote sensing data by a noise-adjusted principal component transform. IEEE Trans. Geosci. Remote Sens. 28(3), 295–304 (1990)

    Google Scholar 

  39. Dabov, K., Foi, A., Egiazarian, K.: Video denoising by sparse 3D transform-domain collaborative filtering,” Proc. 15th European Signal Processing Conference, EUSIPCO 2007, Poznan, Poland, (2007).

  40. Wright, J.A., Yang, A.Y., Ganesh, A., Sastry, S.S., Ma, Y.: Robust face recognition via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 31(2), 210–227 (2009)

    Google Scholar 

  41. Zhang, L., Yang, M., Feng, X.: “Sparse representation or collaborative representation: which helps face recognition?” IEEE International Conference on Computer Vision, pp. 471–478, 2011.

  42. Masayuki Tanka’s Matlab code for face parts detection. Available at: https://www.mathworks.com/matlabcentral/fileexchange/36855-face-parts-detection, accessed on August 18, 2016.

  43. Lee, K.C., Ho, J., Kriegman, D.: Acquiring linear subspaces for face recognition under variable lighting. IEEE Trans. Pattern Anal. Mach. Intell. 27(5), 684–698 (2005)

    Google Scholar 

  44. Sim, T., Baker, S., Bsat, M.: The CMU pose, illumination, and expression database. IEEE Trans. Pattern Anal. Mach. Intell. 25(12), 1615–1618 (2003)

    Google Scholar 

  45. Abdi, H.: Partial least squares regression and projection on latent structure regression (PLS-Regression). Wiley Interdiscip. Rev. Comput. Statist. 2, 97–106 (2010)

    Google Scholar 

  46. Yan, C., Gong, B., Wei, Y., Gao, Y.: Deep Multi-View Enhancement Hashing for Image Retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 43, 1445–1451 (2021)

    Google Scholar 

  47. Yan, C., Li, Z., Zhang, Y., Liu, Y., Ji, X., Zhang, Y.: Depth image denoising using nuclear norm and learning graph model. ACM Trans. Multimed. Comput. Commun. Appl. 16, 122 (2020)

    Google Scholar 

  48. Yan, C., Hao, Y., Li, L., Yin, J., Liu, A., Mao, Z., Chen, Z., Gao, X.: Task-adaptive attention for image captioning. IEEE Trans. Circuits Syst. Video Technol. 32(1), 43–51 (2021)

    Google Scholar 

  49. Yan, C., Teng, T., Liu, Y., Zhang, Y., Wang, H., Ji, X.: Precise no-reference image quality evaluation based on distortion identification. ACM Trans. Multimed. Comput. Commun. Appl. 17, 110 (2021)

    Google Scholar 

  50. Yan, C., Meng, L., Li, L., Zhang, J., Yin, J., Zhang, J., Wang, Z., Zheng, B.: Age-Invariant Face Recognition by Multi-Feature Fusion and Decomposition with Self-Attention. ACM Trans. Multimed. Comput. Commun. Appl. 18, 29 (2022)

    Google Scholar 

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Acknowledgements

The authors would like to thank the editor and the two anonymous reviewers whose comments and suggestions have improved the quality of this paper. The authors thank Dr. Vitomir Struc for posting his Inface toolbox for face recognition under varying illumination environment, and the inventors of the extended Yale-B face dataset and the CMU-PIE face dataset for sharing their face datasets with us. We would like to thank David Zhang, Lei Zhang and Meng Yang for making their PolyU-HSFD dataset and CRC classifier source code available to us. We would also like to thank Pan et al. for giving us access to their CMU-HSFD dataset and Masayuki Tanka for making the MATLAB source code available for face part detection.

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  1. Department of Computer Science and Software Engineering, Concordia University, Montreal, QC, H3G 1M8, Canada

    Guang Yi Chen & Adam Krzyzak

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  1. Guang Yi Chen
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G. Y. Chen programmed the code, wrote the paper. A. Krzyzak edited the paper, supervised the paper.

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Correspondence to Guang Yi Chen.

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Chen, G.Y., Krzyzak, A. Face recognition via selective denoising, filter faces and hog features. SIViP 18, 369–378 (2024). https://doi.org/10.1007/s11760-023-02769-8

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