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

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

Abstract

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.

Keywords

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.
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Notes

Acknowledgement

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

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • M. Ali Akber Dewan
    • 1
  • 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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