SCALE ROBUST HEAD POSE ESTIMATION BASED ON RELATIVE HOMOGRAPHY TRANSFORMATION

Chenguang Liu (), Hengda Cheng () and Aravind Dasu ()
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Chenguang Liu: Computer Science Department, Utah State University, 403 Old Main Hill, Logan, Utah 84322, USA
Hengda Cheng: Computer Science Department, Utah State University, 403 Old Main Hill, Logan, Utah 84322, USA
Aravind Dasu: Energy Dynamics Laboratory, Utah State University Research Foundation, 1695 North Research Park Way, North Logan, Utah 84341, USA

New Mathematics and Natural Computation (NMNC), 2014, vol. 10, issue 01, 69-90

Abstract: Head pose estimation has been widely studied in recent decades due to many significant applications. Different from most of the current methods which utilize face models to estimate head position, we develop a relative homography transformation based algorithm which is robust to the large scale change of the head. In the proposed method, salient Harris corners are detected on a face, and local binary pattern features are extracted around each of the corners. And then, relative homography transformation is calculated by using RANSAC optimization algorithm, which applies homography to a region of interest (ROI) on an image and calculates the transformation of a planar object moving in the scene relative to a virtual camera. By doing so, the face center initialized in the first frame will be tracked frame by frame. Meanwhile, a head shoulder model based Chamfer matching method is proposed to estimate the head centroid. With the face center and the detected head centroid, the head pose is estimated. The experiments show the effectiveness and robustness of the proposed algorithm.

Keywords: Head pose estimation; head orienting; homography transformation; Chamfer matching (search for similar items in EconPapers)
Date: 2014
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DOI: 10.1142/S1793005714500045

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