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Sensors 2015, 15(9), 20945-20966; doi:10.3390/s150920945

Head Pose Estimation on Top of Haar-Like Face Detection: A Study Using the Kinect Sensor

1
Institute for Information Technology and Communications (IIKT), Otto-von-Guericke-University Magdeburg, Magdeburg D-39016, Germany
2
Department of Software Engineering, College of Computer Science and Information Sciences, King Saud University, Riyadh 11451, Saudi Arabia
3
Department of Computer Science, College of Science, Menoufia University, Menoufia 32721, Egypt
*
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 3 July 2015 / Revised: 4 August 2015 / Accepted: 6 August 2015 / Published: 26 August 2015
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [4634 KB, uploaded 2 September 2015]   |  

Abstract

Head pose estimation is a crucial initial task for human face analysis, which is employed in several computer vision systems, such as: facial expression recognition, head gesture recognition, yawn detection, etc. In this work, we propose a frame-based approach to estimate the head pose on top of the Viola and Jones (VJ) Haar-like face detector. Several appearance and depth-based feature types are employed for the pose estimation, where comparisons between them in terms of accuracy and speed are presented. It is clearly shown through this work that using the depth data, we improve the accuracy of the head pose estimation. Additionally, we can spot positive detections, faces in profile views detected by the frontal model, that are wrongly cropped due to background disturbances. We introduce a new depth-based feature descriptor that provides competitive estimation results with a lower computation time. Evaluation on a benchmark Kinect database shows that the histogram of oriented gradients and the developed depth-based features are more distinctive for the head pose estimation, where they compare favorably to the current state-of-the-art approaches. Using a concatenation of the aforementioned feature types, we achieved a head pose estimation with average errors not exceeding 5:1; 4:6; 4:2 for pitch, yaw and roll angles, respectively. View Full-Text
Keywords: head pose; local binary pattern; histogram of gradient; Gabor filter; Kinect sensor; support vector machine; regression head pose; local binary pattern; histogram of gradient; Gabor filter; Kinect sensor; support vector machine; regression
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Saeed, A.; Al-Hamadi, A.; Ghoneim, A. Head Pose Estimation on Top of Haar-Like Face Detection: A Study Using the Kinect Sensor. Sensors 2015, 15, 20945-20966.

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