A Classification Framework for Large-Scale Face Recognition Systems

  • Ziheng Zhou
  • Samuel Chindaro
  • Farzin Deravi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5558)


This paper presents a generic classification framework for large-scale face recognition systems. Within the framework, a data sampling strategy is proposed to tackle the data imbalance when image pairs are sampled from thousands of face images for preparing a training dataset. A modified kernel Fisher discriminant classifier is proposed to make it computationally feasible to train the kernel-based classification method using tens of thousands of training samples. The framework is tested in an open-set face recognition scenario and the performance of the proposed classifier is compared with alternative techniques. The experimental results show that the classification framework can effectively manage large amounts of training data, without regard to feature types, to efficiently train classifiers with high recognition accuracy compared to alternative techniques.


classification framework face recognition and kernel Fisher discriminant 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Ziheng Zhou
    • 1
  • Samuel Chindaro
    • 1
  • Farzin Deravi
    • 1
  1. 1.Department of ElectronicsUniversity of Kent CanterburyUK

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