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Reseach Article

Heterogeneous Face Matching: NIR Images to VIS Images

Published on March 2017 by Sandhya R.waddhavane, S.m.kamalapur
Emerging Trends in Computing
Foundation of Computer Science USA
ETC2016 - Number 3
March 2017
Authors: Sandhya R.waddhavane, S.m.kamalapur
7a1e6229-321f-4061-b1ca-6bbdd6e2ba1b

Sandhya R.waddhavane, S.m.kamalapur . Heterogeneous Face Matching: NIR Images to VIS Images. Emerging Trends in Computing. ETC2016, 3 (March 2017), 5-9.

@article{
author = { Sandhya R.waddhavane, S.m.kamalapur },
title = { Heterogeneous Face Matching: NIR Images to VIS Images },
journal = { Emerging Trends in Computing },
issue_date = { March 2017 },
volume = { ETC2016 },
number = { 3 },
month = { March },
year = { 2017 },
issn = 0975-8887,
pages = { 5-9 },
numpages = 5,
url = { /proceedings/etc2016/number3/27314-6268/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 Emerging Trends in Computing
%A Sandhya R.waddhavane
%A S.m.kamalapur
%T Heterogeneous Face Matching: NIR Images to VIS Images
%J Emerging Trends in Computing
%@ 0975-8887
%V ETC2016
%N 3
%P 5-9
%D 2017
%I International Journal of Computer Applications
Abstract

Heterogeneous (cross spectral) face matching is very important in many of the security applications, especially at night time face recognition where query images are near infrared face images and gallery images are generally visible light images. At night time environment near infrared cameras are used for imaging and images can be captured at various distances as object is not at fix position. As distance increases the quality of face image get degrade and it becomes difficult to match the query near infrared face image with the gallery images. The aim of proposed work is to implement an efficient face matching technique that resolves the problem of cross distance together with cross spectral face matching. Learning based image restoration is an approach to deal with this problem. In this the face images at long distances are restored first and then restored face images are matched with VIS (database) images. The proposed work improves the face matching performance by normalizing near infrared and visible light face images using Difference of Gaussian filter and extracted HOG features for heterogeneous face matching.

References
  1. Sandhya R. Wadhavane and Prof. Dr. S. M. Kamalapur, "Heterogeneous Face Matching: NIR images to VIS images", cPGCON 2016, Fifth Post Graduate Conference of Computer Engineering, March 2016
  2. D. Kang, et al. , Night time face recognition at a large standoff: cross-distance and cross-spectral matching, Pattern Recognition(2014), \http://dx. doi. org/10. 1016/j. patcog. 2014. 06. 004
  3. Zhifeng Li, Senior Member, IEEE, Dihong Gong, Yu Qiao, Senior Member, IEEE, and zacheng Tao, Senior Member," Common Feature Discriminant Analysis for Matching Infrared Face Images to Optical Face Images," June 2014
  4. Dihong Gong and Jiangyu Zheng. 2013. "A Maximum Correlation Feature Descriptor for Heterogeneous Face Recognition". In Asian Conference on Pattern Recognition (ACPR). 135–139
  5. B. Klare and A. K. Jain," Heterogeneous face recognition using kernel prototype similarities," IEEE Trans. Pattern Anal. Mach. Intell. , vol. 35, no. 6, pp. 1410–1422, Jun. 2013
  6. Xiong Pengfei, Lei Huang, and Changping Liu. 2012. "A method for heterogeneous face image synthesis", In The IAPR International Conference on Biometrics (ICB). 1–6.
  7. Likun Huang, Jiwen Lu, and Yap-Peng Tan. 2012. "Learning modality-invariant features for heterogeneous face recognition" ,In International Conference on Pattern Recognition(ICPR). 1683–1686
  8. H. Maeng, S. Liao, D. Kang, S. -W. Lee, A . K. Jain, "Nighttime face recognition at long distance: cross-distance and cross-spectral matching," in: Proceedings of ACCV,2012,pp. 708–721
  9. D. Goswami, Chi-Ho Chan, D. Windridge, and J. Kittler. 2011. "Evaluation of face recognition system in heterogeneous environments (visible vs NIR)", In ICCV. 2160–2167.
  10. H. Maeng, H. C. Choi, U. Park, S. W. Lee, A. K. Jain, NFRAD: near-infrared face recognition at a distance, in: of IJCB,2011,pp. 1–7.
  11. Jie Ch1en, Dong Yi, Jimei Yang, Guoying Zhao, S. Z. Li, and M. Pietikainen. 2009. "Learning mappings for face synthesis from near infrared to visual light images", In CVPR . 156–163.
  12. B. Klare and A. Jain, "Heterogeneous Face Recognition: Matching NIR to Visible Light Images," Proc. Int'l Conf. Pattern Recognition,2010.
  13. S. Liao, D. Yi, Z. Lei, R. Qin, and S. Li. "Heterogeneous face recognition from local structures of normalized appearance", In Proc. 3rd ICB, 2009
  14. Z. Lei and S. Li. "Coupled Spectral Regression for matching heterogeneous faces", In Computer Vision and Pattern Recognition, 2009. CVPR2009. IEEE Conference on, pages 1123–1128, June 2009. 2
  15. R. Wang, J. Yang, D. Yi, and S. Z. Li. "An analysis-by synthesis method for heterogeneous face biometrics", In Proceedings of IAPR/IEEE International Conference on Biometrics,2009. 2
  16. Z. Lei and S. Li. "Coupled Spectral Regression for matching heterogeneous faces", In Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, pages 1123–1128, June 2009. 2
  17. D. Yi, R. Liu, R. -F. Chu, Z. Lei, and S. Z. Li, "Face matching between near infrared and visible light images," in Proc. Int. Conf. Adv. Biometrics, 2007, pp. 523–530
  18. Lin, D. , Tang, X. : "Inter-modality face recognition", In: Leonardis, A. Bischof, H. Pinz, A. (eds. ) ECCV 2006. LNCS, vol. 3954, pp. 13–26. Springer, Heidelberg (2006)
Index Terms

Computer Science
Information Sciences

Keywords

Acquisition System Cross Spectral Cross Distance Heterogeneous Face Image Restoration Near Infrared (nir) Face Image Visible Light (vis) Face Image