Robust head pose estimation using supervised manifold projection

Chao Wang, Xubo Song

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

3 Scopus citations

Abstract

Head poses can be automatically estimated using manifold learning algorithms, with the assumption that with pose being the only variable, the facial images should lie in a smooth and low-dimensional manifold. However, this estimation approach is challenging due to other appearance variations related to identity, head location in image, background, facial expression, and illumination. This problem may be alleviated by incorporating the pose angle information of training samples into the manifold learning process. In this paper, we propose a supervised neighborhood-based linear feature transformation algorithm, which is a variant of Fisher Discriminant Analysis (FDA), to constrain the projection computation of manifold learning. The experimental results show that our algorithm improves the accuracy and robustness of head pose estimation.

Original languageEnglish (US)
Title of host publication2012 IEEE International Conference on Image Processing, ICIP 2012 - Proceedings
Pages161-164
Number of pages4
DOIs
StatePublished - Dec 1 2012
Event2012 19th IEEE International Conference on Image Processing, ICIP 2012 - Lake Buena Vista, FL, United States
Duration: Sep 30 2012Oct 3 2012

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880

Other

Other2012 19th IEEE International Conference on Image Processing, ICIP 2012
CountryUnited States
CityLake Buena Vista, FL
Period9/30/1210/3/12

Keywords

  • Head pose estimation
  • manifold learning
  • projection computation
  • supervised neighborhood-based FDA

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems

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  • Cite this

    Wang, C., & Song, X. (2012). Robust head pose estimation using supervised manifold projection. In 2012 IEEE International Conference on Image Processing, ICIP 2012 - Proceedings (pp. 161-164). [6466820] (Proceedings - International Conference on Image Processing, ICIP). https://doi.org/10.1109/ICIP.2012.6466820