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TinyPoseNet: A Fast and Compact Deep Network for Robust Head Pose Estimation

  • Shanru Li
  • Liping Wang
  • Shuang Yang
  • Yuanquan Wang
  • Chongwen WangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10635)

Abstract

As an inherent attribute of human, head pose plays an important role in many tasks. In this paper, we formulate head pose estimation in different directions as a multi-task regression problem, and propose a fast, compact and robust head pose estimation model, named TinyPoseNet. Specifically, we combine the tasks of head pose estimation in different directions into one joint learning task and design the whole model based on the principle of “being deeper” and “being thinner” to obtain a tiny model with specially designed types and particular small numbers of filters. We perform thorough experiments on 3 types of test sets and compare our method with others from several different aspects, including the accuracy, the speed, the compactness and so on. In addition, we introduce large angle data in Multi-PIE to verify the ability of dealing with large-scale pose in practice. All the experiments demonstrate the advantages of the proposed model.

Keywords

Head pose estimation Deep learning Data augmentation 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Shanru Li
    • 1
  • Liping Wang
    • 2
  • Shuang Yang
    • 3
    • 4
  • Yuanquan Wang
    • 5
  • Chongwen Wang
    • 1
    Email author
  1. 1.School of SoftwareBeijing Institute of TechnologyBeijingChina
  2. 2.Purple Bull FundsBeijingChina
  3. 3.Key Lab of Intelligent Information Processing of Chinese Academy of Sciences(CAS)Institute of Computing Technology, CASBeijingChina
  4. 4.University of Chinese Academy of SciencesBeijingChina
  5. 5.Hebei University of TechnologyTianjinChina

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