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Extended multi-spectral face recognition across two different age groups: an empirical study

Published: 18 December 2016 Publication History

Abstract

Face recognition has attained a greater importance in bio-metric authentication due to its non-intrusive property of identifying individuals at varying stand-off distance. Face recognition based on multi-spectral imaging has recently gained prime importance due to its ability to capture spatial and spectral information across the spectrum. Our first contribution in this paper is to use extended multi-spectral face recognition in two different age groups. The second contribution is to show empirically the performance of face recognition for two age groups. Thus, in this paper, we developed a multi-spectral imaging sensor to capture facial database for two different age groups (≤ 15years and ≥ 20years) at nine different spectral bands covering 530nm to 1000nm range. We then collected a new facial images corresponding to two different age groups comprises of 168 individuals. Extensive experimental evaluation is performed independently on two different age group databases using four different state-of-the-art face recognition algorithms. We evaluate the verification and identification rate across individual spectral bands and fused spectral band for two age groups. The obtained evaluation results shows higher recognition rate for age groups ≥ 20years than ≤ 15years, which indicates the variation in face recognition across the different age groups.

References

[1]
K. Amolins, Y. Zhang, and P. Dare. Wavelet based image fusion techniques - an introduction, review and comparison. {ISPRS} Journal of Photogrammetry and Remote Sensing, 62(4):249--263, 2007.
[2]
R. R. Anderson and J. A. Parrish. Optical Properties of Human Skin, pages 147--194. Springer US, Boston, MA, 1982.
[3]
R. Basri and D. W. Jacobs. Lambertian reflectance and linear subspaces. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(2):218--233, 2003.
[4]
T. Bourlai and B. Cukic. Multi-spectral face recognition: Identification of people in difficult environments. In Intelligence and Security Informatics (ISI), 2012 IEEE International Conference on, pages 196--201, 2012.
[5]
X. Chen, P. J. Flynn, and K. W. Bowyer. Ir and visible light face recognition. J. Comput. Vis. Image Underst, 99:332--358, 2005.
[6]
N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, volume 1, pages 886--893, 2005.
[7]
W. Di, L. Zhang, D. Zhang, and Q. Pan. Studies on hyperspectral face recognition in visible spectrum with feature band selection. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 40(6):1354--1361, 2010.
[8]
E. Eidinger, R. Enbar, and T. Hassner. Age and gender estimation of unfiltered faces. IEEE Transactions on Information Forensics and Security, 9(12):2170--2179, 2014.
[9]
R. S. Ghiass, O. Arandjelović, A. Bendada, and X. Maldague. Infrared face recognition: A comprehensive review of methodologies and databases. Pattern Recognition, 47(9):2807--2824, 2014.
[10]
T. Igarashi, K. Nishino, and S. K. Nayar. The appearance of human skin: A survey. Found. Trends. Comput. Graph. Vis., 3(1):1--95, 2007.
[11]
J. A. Iglesias-Guitian, C. Aliaga, A. Jarabo, and D. Gutierrez. A Biophysically-Based Model of the Optical Properties of Skin Aging. Computer Graphics Forum, 2015.
[12]
S. Z. Li, R. Chu, S. Liao, and L. Zhang. Illumination invariant face recognition using near-infrared images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(4):627--639, 2007.
[13]
F. Nicolo and N. A. Schmid. Long range cross-spectral face recognition: Matching swir against visible light images. IEEE Transactions on Information Forensics and Security, 7(6):1717--1726, 2012.
[14]
V. Ojansivu and J. Heikkilä. Blur Insensitive Texture Classification Using Local Phase Quantization, pages 236--243. Springer Berlin Heidelberg, Berlin, Heidelberg, 2008.
[15]
A. Oliva and A. Torralba. Modeling the shape of the scene: A holistic representation of the spatial envelope. International Journal of Computer Vision, 42(3):145--175, 2001.
[16]
G. Pajares and J. M. de la Cruz. A wavelet-based image fusion tutorial. Pattern Recognition, 37(9):1855--1872, 2004.
[17]
P. E. Pochi, J. S. Strauss, and D. T. Downing. Age-related changes in sebaceous gland activity. Journal of Investigative Dermatology, 73(1):108--111, 1979.
[18]
M. Störring. Computer vision and human skin colour. In Ph.D. dissertation (Computer Vision and Media Technology Laboratory, Aalborg University, Denmark, 2004.
[19]
A. K. J. Unsang Park. Face Aging Modeling, pages 251--274. Springer-Verlag London, 2011.
[20]
M. Uzair, A. Mahmood, and A. Mian. Hyperspectral face recognition with spatiospectral information fusion and pls regression. IEEE Transactions on Image Processing, 24(3):1127--1137, 2015.
[21]
J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma. Robust face recognition via sparse representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(2):210--227, 2009.
[22]
Z. Xiao, C. Guo, Y. Ming, and L. Qiang. Research on log gabor wavelet and its application in image edge detection. In Signal Processing, 2002 6th International Conference on, volume 1, pages 592--595 vol.1, 2002.
[23]
L. Zhang, M. Yang, and X. Feng. Sparse representation or collaborative representation: Which helps face recognition? In Computer Vision (ICCV), 2011 IEEE International Conference on, pages 471--478, 2011.

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  • (2024)Does fusion of complementary spectral bands improves the cross-illumination on the performance of gender prediction?2024 27th International Conference on Information Fusion (FUSION)10.23919/FUSION59988.2024.10706416(1-8)Online publication date: 8-Jul-2024
  • (2020)Intraspectrum Discrimination and Interspectrum Correlation Analysis Deep Network for Multispectral Face RecognitionIEEE Transactions on Cybernetics10.1109/TCYB.2018.287659150:3(1009-1022)Online publication date: Mar-2020
  • (2018)Detecting Glass in Ocular Region Based on Grassmann Manifold Projection Metric Learning by Exploring Spectral Imaging2018 14th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)10.1109/SITIS.2018.00026(106-113)Online publication date: Nov-2018
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      cover image ACM Other conferences
      ICVGIP '16: Proceedings of the Tenth Indian Conference on Computer Vision, Graphics and Image Processing
      December 2016
      743 pages
      ISBN:9781450347532
      DOI:10.1145/3009977
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Sponsors

      • Google Inc.
      • QI: Qualcomm Inc.
      • Tata Consultancy Services
      • NVIDIA
      • MathWorks: The MathWorks, Inc.
      • Microsoft Research: Microsoft Research

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 18 December 2016

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      Author Tags

      1. collaborative representation
      2. face recognition
      3. facial age groups
      4. feature extraction
      5. multi-spectral imaging

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      ICVGIP '16
      Sponsor:
      • QI
      • MathWorks
      • Microsoft Research

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      ICVGIP '16 Paper Acceptance Rate 95 of 286 submissions, 33%;
      Overall Acceptance Rate 95 of 286 submissions, 33%

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      View all
      • (2024)Does fusion of complementary spectral bands improves the cross-illumination on the performance of gender prediction?2024 27th International Conference on Information Fusion (FUSION)10.23919/FUSION59988.2024.10706416(1-8)Online publication date: 8-Jul-2024
      • (2020)Intraspectrum Discrimination and Interspectrum Correlation Analysis Deep Network for Multispectral Face RecognitionIEEE Transactions on Cybernetics10.1109/TCYB.2018.287659150:3(1009-1022)Online publication date: Mar-2020
      • (2018)Detecting Glass in Ocular Region Based on Grassmann Manifold Projection Metric Learning by Exploring Spectral Imaging2018 14th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)10.1109/SITIS.2018.00026(106-113)Online publication date: Nov-2018
      • (2018)Robust gender classification using extended multi-spectral imaging by exploring the spectral angle mapper2018 IEEE 4th International Conference on Identity, Security, and Behavior Analysis (ISBA)10.1109/ISBA.2018.8311455(1-8)Online publication date: Jan-2018
      • (2018)Deep Learning-based Face Recognition and the Robustness to Perspective Distortion2018 24th International Conference on Pattern Recognition (ICPR)10.1109/ICPR.2018.8545037(3445-3450)Online publication date: Aug-2018
      • (2017)Band level fusion using quaternion representation for extended multi-spectral face recognition2017 20th International Conference on Information Fusion (Fusion)10.23919/ICIF.2017.8009750(1-6)Online publication date: Jul-2017
      • (2017)Collaborative representation of Grassmann manifold projection metric for robust multi-spectral face recognitionProceedings of the 10th International Conference on Security of Information and Networks10.1145/3136825.3136884(117-124)Online publication date: 13-Oct-2017

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