Advertisement

Springer Nature is making Coronavirus research free. View research | View latest news | Sign up for updates

Open-set single-sample face recognition in video surveillance using fuzzy ARTMAP

  • 167 Accesses

  • 2 Citations

Abstract

Single-sample face recognition has been investigated by a few researches over the past few decades. However, due to the demand of identity of interest searching from video surveillance in recent years, this system has been expanded to open-set face recognition (OSFR) scheme. The OSFR system provides the identity of registered subjects and rejects the unregistered ones using only single-sample reference. This is important in video surveillance applications in which both database members and non-members are expected to appear in the scene. In this paper, we propose to use fuzzy ARTMAP neural network to solve the problem of open-set single-sample face recognition in real-world video surveillance scenario. Our proposed approach can recognize faces in near-frontal views under various illumination and facial expression conditions. Facial features are extracted using histograms of oriented gradients and Gabor wavelets and then fused using canonical correlation analysis to yield feature vectors that are robust against the aforementioned conditions. The fuzzy ARTMAP classifier has been trained using only single sample per person. We have conducted experiments on three challenging benchmark datasets, namely AR, FRGC, and ChokePoint. The experimental results have shown that the proposed approach has a superior performance than the state-of-the-art approaches.

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

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

References

  1. 1.

    Scheirer WJ, de Rezende Rocha A, Sapkota A, Boult TE (2013) Toward open set recognition. IEEE Trans Pattern Anal Mach Intell 35(7):1757–1772

  2. 2.

    Zhang B, Hao H (2014) Open-set face recognition by transductive kernel associative memory. In: 2014 7th international congress on image and signal processing (CISP), pp 633–638. IEEE

  3. 3.

    Chen C, Zhan Y, Wen C (2009) Hierarchical face recognition based on SVDD and SVM. In: International conference on environmental science and information application technology, 2009. ESIAT 2009, vol 2, pp 692–695. IEEE

  4. 4.

    dos Santos Jr CE, Schwartz WR (2014) Extending face identification to open-set face recognition. In: 2014 27th SIBGRAPI conference on graphics, patterns and images (SIBGRAPI), pp 188–195. IEEE

  5. 5.

    Qiu J, Zhang Y, Sun J(2013) Face recognition in open world environment. In: Visual communications and image processing (VCIP), 2013, pp 1–6. IEEE

  6. 6.

    Kamgar-Parsi B, Lawson W, Kamgar-Parsi B (2011) Toward development of a face recognition system for watchlist surveillance. IEEE Trans Pattern Anal Mach Intell 33(10):1925–1937

  7. 7.

    Ekenel H, Szasz-Toth L, Stiefelhagen R (2009) Open-set face recognition-based visitor interface system. Comput Vis Syst 5815:43–52

  8. 8.

    Toufiq R, Islam MdR (2014) Face recognition system using PCA-ANN technique with feature fusion method. In: 2014 international conference on electrical engineering and information and communication technology (ICEEICT), pp 1–5. IEEE

  9. 9.

    Zhang B (2012) Reliable face recognition by random subspace support vector machine ensemble. In: 2012 international conference on machine learning and cybernetics (ICMLC), vol 1, pp 415–420. IEEE

  10. 10.

    Theodorakopoulos I, Rigas I, Economou G, Fotopoulos S (2011) Face recognition via local sparse coding. In: 2011 IEEE international conference on computer vision (ICCV), pp 1647–1652. IEEE

  11. 11.

    Chen J-C, Shi S-Y, Lien J-JJ (2010) Face recognition and unseen subject rejection in margin-enhanced space. In: 2010 international conference on system science and engineering (ICSSE), pp 631–636. IEEE

  12. 12.

    Nakamura K, Takano H (2007) Unregistered face discrimination by the face orientation and size recognition. In: International joint conference on neural networks, 2007. IJCNN 2007, pp 1924–1928. IEEE

  13. 13.

    Li F, Wechsler H (2005) Open set face recognition using transduction. IEEE Trans Pattern Anal Mach Intell 27(11):1686–1697

  14. 14.

    Tan X, Chen S, Zhou Z-H, Zhang F (2006) Face recognition from a single image per person: a survey. Pattern Recogn 39(9):1725–1745

  15. 15.

    Pagano C, Granger E, Sabourin R, Marcialis GL, Roli F (2014) Adaptive ensembles for face recognition in changing video surveillance environments. Inf Sci 286:75–101

  16. 16.

    Haghighat M, Abdel-Mottaleb M, Alhalabi W (2016) Fully automatic face normalization and single sample face recognition in unconstrained environments. Expert Syst Appl 47:23–34

  17. 17.

    Pei T, Zhang L, Wang B, Li F, Zhang Z (2017) Decision pyramid classifier for face recognition under complex variations using single sample per person. Pattern Recogn 64:305–313

  18. 18.

    Ding C, Bao T, Karmoshi S, Zhu M (2017) Single sample per person face recognition with KPCANet and a weighted voting scheme. Signal Image Video Process 11(7):1213–1220

  19. 19.

    Junlin H (2017) Discriminative transfer learning with sparsity regularization for single-sample face recognition. Image Vis Comput 60:48–57

  20. 20.

    Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: IEEE computer society conference on computer vision and pattern recognition, 2005. CVPR 2005, vol 1, pp 886–893. IEEE

  21. 21.

    Liu C, Wechsler H (2002) Gabor feature based classification using the enhanced Fisher linear discriminant model for face recognition. IEEE Trans Image Process 11(4):467–476

  22. 22.

    Turk M, Pentland A (1991) Eigenfaces for recognition. J Cogn Neurosci 3(1):71–86

  23. 23.

    Sun Q-S, Zeng S-G, Liu Y, Heng P-A, Xia D-S (2005) A new method of feature fusion and its application in image recognition. Pattern Recogn 38(12):2437–2448

  24. 24.

    Carpenter GA, Grossberg S, Markuzon N, Reynolds JH, Rosen DB (1992) Fuzzy ARTMAP: a neural network architecture for incremental supervised learning of analog multidimensional maps. IEEE Trans Neural Netw 3(5):698–713

  25. 25.

    Carpenter GA, Grossberg S (2002) Adaptive resonance theory. In: Arbib MA (ed) The handbook of brain theory and neural networks, 2nd edn. MIT Press, London

  26. 26.

    Martinez AM (1998) The AR face database. CVC Technical Report

  27. 27.

    Phillips PJ, Flynn PJ, Scruggs T, Bowyer KW, Chang J, Hoffman K, Marques J, Min J, Worek W (2005) Overview of the face recognition grand challenge. In: IEEE computer society conference on computer vision and pattern recognition, 2005. CVPR 2005, vol 1, pp 947–954. IEEE

  28. 28.

    Wong Y, Chen S, Mau S, Sanderson C, Lovell BC (2011) Patch-based probabilistic image quality assessment for face selection and improved video-based face recognition. In: 2011 IEEE computer society conference on computer vision and pattern recognition workshops (CVPRW), pp 74–81. IEEE

  29. 29.

    Fawcett T (2006) An introduction to ROC analysis. Pattern Recogn Lett 27(8):861–874

  30. 30.

    Viola P, Jones M (2001) Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE computer society conference on computer vision and pattern recognition, 2001. CVPR 2001, vol 1, pp I–I. IEEE

Download references

Acknowledgements

This work is fully supported by the Malaysia Ministry of Higher Education (MOHE) Fundamental Research Grant Scheme (FRGS) No. 203/PELECT/6071294.

Author information

Correspondence to Shahrel Azmin Suandi.

Ethics declarations

Conflict of interest

The authors declare that there is no conflict of interest.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Al-Obaydy, W.N.I., Suandi, S.A. Open-set single-sample face recognition in video surveillance using fuzzy ARTMAP. Neural Comput & Applic 32, 1405–1412 (2020). https://doi.org/10.1007/s00521-018-3649-0

Download citation

Keywords

  • Open-set single-sample face recognition
  • Video surveillance
  • Fuzzy ARTMAP
  • Identity of interest (IoI)