Identity- and illumination-robust head pose estimation using manifold learning

Chao Wang, Xubo Song

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Head pose estimation using manifold learning is challenging due to other appearance variations such as identity and illumination changes. To address the problem, we propose to incorporate supervised information (pose angles of training samples) into the process of manifold learning. Most manifold learning algorithms have two common variables: inter-point distances and graph weights, which can greatly affect the property of the constructed manifold. We propose to redefine these variables by constraining them with the pose angle information. In addition, since the environmental illuminations are distributed in the low-frequency component of the image and the texture-based feature is irrelevant to the pose variation, we use the proposed Localized Edge Orientation Histogram (LEOH) rather than the pixel intensity feature for manifold learning. The experimental results show that our method has the highest estimating accuracy and is robust to identity and illumination.

Original languageEnglish (US)
Title of host publicationProceedings of the 2012 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2012
Pages535-540
Number of pages6
StatePublished - Dec 1 2012
Event2012 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2012 - Las Vegas, NV, United States
Duration: Jul 16 2012Jul 19 2012

Publication series

NameProceedings of the 2012 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2012
Volume2

Other

Other2012 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2012
CountryUnited States
CityLas Vegas, NV
Period7/16/127/19/12

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Keywords

  • Graph weight
  • Inter-point distance
  • Localized edge orientation histogram
  • Robust head pose estimation
  • Supervised manifold learning

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition

Cite this

Wang, C., & Song, X. (2012). Identity- and illumination-robust head pose estimation using manifold learning. In Proceedings of the 2012 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2012 (pp. 535-540). (Proceedings of the 2012 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2012; Vol. 2).