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Fasthpe : a recipe for quick head pose estimation

By Michael Sapienza, Kenneth P. Camilleri and Alexandra Bonnici

Abstract

Estimating the head orientation of a person from a single camera is an important step for human-computer interaction, especially for widely available laptops and hand-held devices. This work aims to track a human face and estimate its orientation in the 6 degrees of freedom from an uncalibrated monocular camera, keeping the user free of any devices or wires. We propose a novel algorithm based on existing computer vision techniques for a real-time (2ms) head pose estimation system, which can start and recover from failure automatically without any previous knowledge of the user’s appearance or location. We demonstrate that this computationally efficient pose estimation system is able to track the continuous roll, yaw, and pitch angles within absolute errors of 3.03, 5.27 and 3.91 degrees respectively. We show that with the tracking of only four face features, it is possible to obtain continuous head orientation measurements in real-time (2ms)

Topics: Face feature tracking, Human-computer interaction
Year: 2014
OAI identifier: oai:www.um.edu.mt:123456789/859
Provided by: OAR@UM

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Citations

  1. (2003). 3d head tracking using non-linear optimization,” in doi
  2. (2005). A real-time stereo head pose tracking system,” in doi
  3. (2001). Active shape models,” doi
  4. (1996). Computing 3-d head orientation from a monocular image sequence,” in doi
  5. (2000). Detection and tracking of facial features in video sequences,” doi
  6. (1994). Determining the gaze of faces in images,” doi
  7. (1996). Example-based head tracking,” in doi
  8. (2008). Face and facial feature detection evaluation - performance evaluation of public domain haar detectors for face and facial feature detection,” in doi
  9. (2000). Fast, reliable head tracking under varying illumination: An approach based on registration of texture-mapped 3d models,” doi
  10. (2008). Generalized adaptive view-based appearance model integrated framework for monocular head pose estimation,” in doi
  11. (2004). Head pose estimation by nonlinear manifold learning,” in doi
  12. (2009). Head pose estimation in computer vision: A survey,” doi
  13. (2002). Head pose estimation using view based eigenspaces,” in doi
  14. (2000). Head pose estimation without manual initialization,”
  15. (2001). Kernel machine based learning for multi-view face detection and pose estimation,” in doi
  16. (1998). Real-time face pose estimation,” doi
  17. (2002). Real-time head orientation estimation using neural networks,” in doi
  18. (2004). Robust real-time detection, tracking, and pose estimation of faces in video streams,” in doi
  19. (2004). Robust real-time face detection,” doi
  20. (2000). The OpenCV Library,” Dr. doi

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