Challenges and Research Directions for Adaptive Biometric Recognition Systems

  • Norman Poh
  • Rita Wong
  • Josef Kittler
  • Fabio Roli
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5558)


Biometric authentication using mobile devices is becoming a convenient and important means to secure access to remote services such as telebanking and electronic transactions. Such an application poses a very challenging pattern recognition problem: the training samples are often sparse and they cannot represent the biometrics of a person. The query features are easily affected by the acquisition environment, the user’s accessories, occlusions and aging. Semi-supervised learning – learning from the query/test data – can be a means to tap the vast unlabeled training data. While there is evidence that semi-supervised learning can work in text categorization and biometrics, its application on mobile devices remains a great challenge. As a preliminary, yet, indispensable study towards the goal of semi-supervised learning, we analyze the following sub-problems: model adaptation, update criteria, inference with several models and user-specific time-dependent performance assessment, and explore possible solutions and research directions.


Mobile Device Gaussian Mixture Model Unlabeled Data Head Orientation Biometric Authentication 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Norman Poh
    • 1
  • Rita Wong
    • 1
  • Josef Kittler
    • 1
  • Fabio Roli
    • 2
  1. 1.University of Surrey, GuildfordSurreyUK
  2. 2.Department of Electrical and Electronic EngineeringUniversity of Cagliari Piazza d’ArmiCagliariItaly

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