Skip to main content
SpringerLink
Log in

Modality identification for heterogeneous face recognition

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Identifying the type of modalities of the query image which can be of types visual, NIR, digital camera, web camera etc. have been assumed to be available before face matching. This leads to a major drawback in achieving fully automated heterogeneous face recognition as real world scenarios cannot be reflected. Therefore, modality identification is an important component of the heterogeneous face recognition system which is being overlooked by majority of the state-of-the-art methods. This component should be given similar attention when comparing with other face recognition modules identifying pose, gesture, camera source etc. In this paper inspired from sensor pattern noise (SPN) estimation based approaches, a novel image sharpening based modality pattern noise technique is proposed for modality identification. The proposed system has been evaluated on three challenging benchmarks of heterogeneous face databases. The proposed technique has produced outstanding results and will open new avenues of research for automated HFR methods in future.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Cai X, Wang C, Xiao B, Chen X, Lv Z, Shi Y (2013) Coupled latent least squares regression for heterogeneous face recognition. IEEE Inter Conf Image Process:2772–2776

  2. Chen M, Fridrich J, Goljan M, Luks̈ J (2008) Determining image origin and integrity using sensor noise. IEEE Trans Inf Forensics Secur 3:74–90

    Article  Google Scholar 

  3. Chingovska I, Anjos A, Marcel S (2013) Anti-spoofing in action joint operation with a verification system. In: IEEE International on Computer Vision and Pattern Recognition Workshops (CVPRW), pp 98–104

  4. de Freitas Pereira T, Anjos A, de Martino JM, Marcel S (2012) LBP-TOP based countermeasure against face spoofing attacks. Computer Vision-ACCV Workshops:121–132

  5. de Freitas Pereira T, Anjos A, de Martino JM, Marcel S (2013) Can face anti-spoofing countermeasures work in a real world scenario. In: IEEE International conference on Biometrics (ICB), pp 1–8

  6. Galbally J, Marcel S, Fierrez J (2014) Image quality assessment for fake biometric detection application to iris, fingerprint, and face recognition. IEEE Trans Image Process 23:710–724

    Article  MathSciNet  Google Scholar 

  7. Garcia JO, Fierrez J, Fernandez FA, Galbally J, Freire MR, Gonzalez-Rodriguez J, Garcia-Mateo C, Alba-Castro JL, Gonzalez-Agulla E, Otero-Muras E, Garcia-Salicetti S, Allano L, Ly-Van B, Dorizzi B, Kittler J, Bourlai T, Poh N, Deravi F, Ng M, Fairhurst M, Hennebert J, Humm A, Tistarelli M, Brodo L, Richiardi J, Drygajlo A, Ganster H, Sukno FM, Pavani SK, Frangi A, Akarun L, Savran A (2010) The multiscenario multienvironment biosecure multimodal database (bmdb). IEEE Trans Pattern Anal Mach Intell 32:1097–1111

    Article  Google Scholar 

  8. Han J, Shao L, Xu D, Shotton J (2013) Enhanced computer vision with microsoft kinect sensor A review. IEEE Trans Cybernetics 43:1318–1334

    Article  Google Scholar 

  9. Huang X, Lei Z, Wang X, Li SZ (2013) Regularized discriminative spectral regression method for heterogeneous face matching. IEEE Trans Image Process 22:1513–1516

    MathSciNet  Google Scholar 

  10. Jain AK, Li SZ (2005) Handbook of Face Recognition. Springer, NewYork

    MATH  Google Scholar 

  11. Kang X, Li Y, Qu Z, Huang J (2012) Enhancing source camera identification performance with a camera reference phase sensor pattern noise. IEEE Trans Inf Forensics Secur 7:393–402

    Article  Google Scholar 

  12. Klare BF, Jain AK (2013) Heterogeneous face recognition using kernel prototype similarities. IEEE Trans Pattern Anal Mach Intell 35:1410–1422

    Article  Google Scholar 

  13. Kollreider K, Fronthaler H, Bigun J (2009) Non-intrusive liveness detection by face images. Image Vision Computing J 27:233244

    Article  Google Scholar 

  14. Komulainen J, Hadid A, Pietikainen M, Anjos A, Marcel S (2013) Complementary countermeasures for detecting scenic face spoofing attacks. In: International Conference on Biometrics (ICB), pp 1–7

  15. Kose N, Dugelay JL (2013) Countermeasure for the protection of face recognition systems against mask attacks. In: IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, pp 1–6

  16. Lawgaly A, Khelifi F, Bouridane A (2013) Image sharpening for efficient source camera identification based on sensor pattern noise estimation. In: Fourth IEEE International Conference In Emerging Security Technologies (EST), pp 113–116

  17. Lee YH, Park SY (1990) A study of convex/concave edges and edge-enhancing operators based on the laplacian. IEEE Trans Circuits and Syst 37:940–946

    Article  MathSciNet  Google Scholar 

  18. Lei Z, Pietikainen M, Li SZ (2014) Learning discriminant face descriptor. IEEE Trans Pattern Anal Mach Intell 36:289–302

    Article  Google Scholar 

  19. Lei Z, Liao S, Jain A.K, Li SZ (2012) Coupled discriminant analysis for heterogeneous face recognition. IEEE Trans Inf Forensics Secur 7:1707–1716

    Article  Google Scholar 

  20. Li CT (2010) Source camera identification using enhanced sensor pattern noise. IEEE Trans Inf Forensics Secur 5:280–287

    Article  Google Scholar 

  21. Li SZ, Lei Z, Ao M (2009) The HFB face database for heterogeneous face biometrics research

  22. Li SZ, Yi D, Lei Z, Liao S (2013) The CASIA NIR-VIS 2.0 face database. In: IEEE International Conference In Computer Vision and Pattern Recognition Workshops, pp 348–353

  23. Li Z, Gong D, Qiao Y, Tao D (2014) Common feature discriminant analysis for matching infrared face images to optical face images. IEEE Trans Image Process 23:2436–2445

    Article  MathSciNet  Google Scholar 

  24. Lin D, Tang X (2006) Inter-modality face recognition. European Conference on Computer Vision:13–26

  25. Liu L, Shao L (2013) Learning discriminative representations from RGB-D video data. In: International Joint Conference on Artificial Intelligence

  26. Liu L, Shao L, Li X, Lu K (2016) Learning spatio-temporal representations for action recognition: A genetic programming approach. IEEE Trans Cybernetics 46:158–170

    Article  Google Scholar 

  27. Lukas J, Fridrich J, Goljan M (2006) Digital camera identification from sensor pattern noise. IEEE Trans Inf Forensics Secur 1:205–214

    Article  Google Scholar 

  28. Mtt J, Hadid A, Pietikainen M (2011) Face spoofing detection from single images using micro-texture analysis. In: IEEE International joint conference on Biometrics, pp 1–7

  29. Pan G, Sun L, Wu Z, Lao S (2007) Eyeblink-based anti-spoofing in face recognition from a generic webcamera. In: IEEE 11th International Conference on Computer Viion (ICCV), pp 1–8

  30. Pavlidis I, Symosek P (2000) The imaging issue in an automatic face/disguise detection system. In: IEEE Workshop on Computer Vision Beyond the Visible Spectrum: Methods and Applications, pp 15– 24

  31. Shao L, Wu D, Li X (2014) Learning deep and wide: A spectral method for learning deep networks. IEEE Transactions on Neural Networks and Learning Systems 25:2303–2308

    Article  Google Scholar 

  32. Sutcu Y, Bayram S, Sencar HT, Memon N (2007) Improvements on sensor noise based source camera identification. In: IEEE International Conference on Multimedia and Expo, pp 24–27

  33. Van Lanh T, Chong KS, Emmanuel S, Kankanhalli MS (2007) A survey on digital camera image forensic methods. In: IEEE International Conference on Multimedia and Expo, pp 16–19

  34. Wang X, Tang X (2009) Face photo-sketch synthesis and recognition. IEEE Trans Pattern Anal Mach Intell 31:1955–1967

    Article  Google Scholar 

  35. Yi D, Lei Z, Li SZ (2015) Shared representation learning for heterogenous face recognition. In: Proceedings of 11th IEEE international conference on automatic face and gesture recognition

  36. Yu M, Liu L, Shao L (2015) Structure-preserving binary representations for RGB-D action recognition. IEEE Trans Pattern Anal Mach Intell

  37. Zhang Z, Yan J, Liu S, Lei Z, Yi D, Li SZ (2012) A face antispoofing database with diverse attacks. In: IEEE International conference on Biometrics (ICB), pp 26–31

  38. Zhu JY, Zheng WS, Lai JH, Li SZ (2014) Matching NIR face to VIS face using transduction. IEEE Trans Inf Forensics Secur 9:501–514

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Digital Technologies, Northumbria University at Newcastle, Newcastle, UK

    Muhammad Khurram Shaikh, Ashref Lawgaly, Muhammad Atif Tahir & Ahmed Bouridane

  2. Systems Research Laboratory Computer Science, FAST- National University of Computer and Emerging Sciences, Karachi, Pakistan

    Muhammad Atif Tahir

Authors
  1. Muhammad Khurram Shaikh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ashref Lawgaly
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Muhammad Atif Tahir
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ahmed Bouridane
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Muhammad Khurram Shaikh.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shaikh, M.K., Lawgaly, A., Tahir, M.A. et al. Modality identification for heterogeneous face recognition. Multimed Tools Appl 76, 4635–4650 (2017). https://doi.org/10.1007/s11042-016-3635-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-016-3635-4

Keywords

Search

Navigation