A Multitask Learning Approach to Face Recognition Based on Neural Networks

  • Feng Jin
  • Shiliang Sun
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5326)


For traditional human face based biometrics, usually one task (face recognition) is learned at one time. This single task learning (STL) approach may neglect potential rich information resources hidden in other related tasks, while multitask learning (MTL) can make full use of the latent information. MTL is an inductive transfer method which improves generalization by using the domain information contained in the training signals of related tasks as an inductive bias. In this paper, backpropagation (BP) network based MTL approach is proposed for face recognition. The feasibility of this approach is demonstrated through two different face recognition experiments, which show that MTL based on BP neural networks is more effective than the traditional STL approach, and that MTL is also a practical approach for face recognition.


multitask learning (MTL) single task learning (STL) face recognition backpropagation (BP) artificial neural network (ANN) 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Feng Jin
    • 1
  • Shiliang Sun
    • 1
  1. 1.Department of Computer Science and TechnologyEast China Normal UniversityShanghaiP.R. China

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