An Approach to Improving Single Sample Face Recognition Using High Confident Tracking Trajectories

  • M. Ali Akber DewanEmail author
  • Dan Qiao
  • Fuhua Lin
  • Dunwei Wen
  • Kinshuk
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9673)


In this paper, single sample face recognition (SSFR) problem is addressed by introducing an adaptive biometric system within a modular architecture where one detector per target individual is proposed. For each detector, a face model is generated with the gallery face image and updated overtime. Sequential Karhunen-Loeve technique is applied to update the face model using representative face captures which are selected from the operational data by using reliable tracking trajectories. This process helps to induce intra-class variation of face appearance and improve representativeness of the face models. The effectiveness of the proposed method is detailed in security surveillance and user authentication using Chokepoint and FIA datasets in SSFR setting.


Face Recognition Face Image User Authentication Face Model Target Person 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This research is supported by Academic Research Fund and Research Incentive Grant, Athabasca University, and NSERC, Canada.


  1. 1.
    Puvvadi, U.L.N., Benedetto, K.D., Patil, A., Kang, K.-D., Park, Y.: Cost-effective security support in real-time video surveillance. IEEE Trans. Industr. Inf. 11(6), 1457–1465 (2015)CrossRefGoogle Scholar
  2. 2.
    Tan, X., Chen, S., Zhou, Z.-H., Zhang, F.: Face recognition from a single image per person: a survey. Pattern Recogn. 39(9), 1725–1745 (2006)CrossRefzbMATHGoogle Scholar
  3. 3.
    Dewan, A., Granger, E., Roli, F., Sabourin, R., Marcialis, G.: Adaptive appearance model tracking for still-to-video face recognition. Pattern Recogn. 49, 129–151 (2016)CrossRefGoogle Scholar
  4. 4.
    Ross, D.A., Lim, J., Lin, R.-S., Yang, M.-H.: Incremental learning for robust visual tracking. Int. J. Comput. Vis. 77(1), 125–141 (2008)CrossRefGoogle Scholar
  5. 5.
    Levy, A., Lindenbaum, M.: Sequential Karhunen–Loeve basis extraction and its application to images. IEEE Trans. Image Process. 9(8), 1371–1374 (2000)CrossRefzbMATHGoogle Scholar
  6. 6.
    Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vis. 57(2), 137–154 (2004)CrossRefGoogle Scholar
  7. 7.
    Zhang, T., Li, X., Guo, R.-Z.: Producing virtual face images for single sample face recognition. Optik-Int. J. Light Electron Opt. 125(17), 5017–5024 (2014)CrossRefGoogle Scholar
  8. 8.
    Bashbagi, S., Granger, E., Sabourin, R., Bilodeau, G.-A.: Watch-list screening using ensembles based on multiple face representations. In: ICPR, Stockholm, Sweden (2014)Google Scholar
  9. 9.
    Wong, Y., Chen, S., Mau, S., Sanderson, C., Lovell, B.C.: Patch-based probabilistic image quality assessment for face selection and improved video-based face recognition. In: CVPRW, Colorado, USA (2011)Google Scholar
  10. 10.
    Goh, R., Liu, L., Liu, X., Chen, T.: The CMU face in action database. In: AMFG, Beijing, China (2005)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • M. Ali Akber Dewan
    • 1
    Email author
  • Dan Qiao
    • 2
  • Fuhua Lin
    • 1
  • Dunwei Wen
    • 1
  • Kinshuk
    • 1
  1. 1.School of Computing and Information SystemsAthabasca UniversityEdmontonCanada
  2. 2.Department of Mechanical EngineeringUniversity of AlbertaEdmontonCanada

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