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Evaluation of Head Pose Estimation for Studio Data

  • Jilin Tu
  • Yun Fu
  • Yuxiao Hu
  • Thomas Huang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4122)

Abstract

This paper introduces our head pose estimation system that localizes nose-tip of the faces and estimate head poses in studio quality pictures. After the nose-tip in the training data are manually labeled, the appearance variation caused by head pose changes is characterized by tensor model. Given images with unknown head pose and nose-tip location, the nose-tip of the face is localized in a coarse-to-fine fashion, and the head pose is estimated simultaneously by the head pose tensor model. The image patches at the localized nose tips are then cropped and sent to two other head pose estimators based on LEA and PCA techniques. We evaluated our system on the Pointing’04 head pose image database. With the nose-tip location known, our head pose estimators can achieve 94~96% head pose classification accuracy(within ±15 o ). With nose-tip unknown, we achieves 85% nose-tip localization accuracy(within 3 pixels from the ground truth), and 81~84% head pose classification accuracy(within ±15 o ).

Keywords

Tilt Angle Image Patch Tensor Model Laplacian Pyramid Core Tensor 
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 Berlin Heidelberg 2007

Authors and Affiliations

  • Jilin Tu
    • 1
  • Yun Fu
    • 1
  • Yuxiao Hu
    • 1
  • Thomas Huang
    • 1
  1. 1.Beckman Institute, University of Illinois at Urbana-Champaign, 405 N Mathews Ave, Urbana, IL 61801USA

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