Facial Landmark Detection by Deep Multi-task Learning

  • Zhanpeng Zhang
  • Ping Luo
  • Chen Change Loy
  • Xiaoou Tang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8694)

Abstract

Facial landmark detection has long been impeded by the problems of occlusion and pose variation. Instead of treating the detection task as a single and independent problem, we investigate the possibility of improving detection robustness through multi-task learning. Specifically, we wish to optimize facial landmark detection together with heterogeneous but subtly correlated tasks, e.g. head pose estimation and facial attribute inference. This is non-trivial since different tasks have different learning difficulties and convergence rates. To address this problem, we formulate a novel tasks-constrained deep model, with task-wise early stopping to facilitate learning convergence. Extensive evaluations show that the proposed task-constrained learning (i) outperforms existing methods, especially in dealing with faces with severe occlusion and pose variation, and (ii) reduces model complexity drastically compared to the state-of-the-art method based on cascaded deep model [21].

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Zhanpeng Zhang
    • 1
  • Ping Luo
    • 1
  • Chen Change Loy
    • 1
  • Xiaoou Tang
    • 1
  1. 1.Dept. of Information EngineeringThe Chinese University of Hong KongHong KongChina

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