Efficient illumination independent appearance-based face tracking

Buenaposada Biencinto, José Miguel and Muñoz, Enrique and Baumela Molina, Luis (2009). Efficient illumination independent appearance-based face tracking. "Image and Vision Computing", v. 27 (n. 5); pp. 560-578. ISSN 0262-8856.


Title: Efficient illumination independent appearance-based face tracking
  • Buenaposada Biencinto, José Miguel
  • Muñoz, Enrique
  • Baumela Molina, Luis
Item Type: Article
Título de Revista/Publicación: Image and Vision Computing
Date: 2 April 2009
Volume: 27
Faculty: Facultad de Informática (UPM)
Department: Inteligencia Artificial
Creative Commons Licenses: None

Full text

PDF - Requires a PDF viewer, such as GSview, Xpdf or Adobe Acrobat Reader
Download (2MB) | Preview

Alternative locations

Official URL: http://dx.doi.org/10.1016/j.imavis.2008.04.015


One of the major challenges that visual tracking algorithms face nowadays is being able to cope with changes in the appearance of the target during tracking. Linear subspace models have been extensively studied and are possibly the most popular way of modelling target appearance. We introduce a linear subspace representation in which the appearance of a face is represented by the addition of two approxi- mately independent linear subspaces modelling facial expressions and illumination respectively. This model is more compact than previous bilinear or multilinear ap- proaches. The independence assumption notably simplifies system training. We only require two image sequences. One facial expression is subject to all possible illumina- tions in one sequence and the face adopts all facial expressions under one particular illumination in the other. This simple model enables us to train the system with no manual intervention. We also revisit the problem of efficiently fitting a linear subspace-based model to a target image and introduce an additive procedure for solving this problem. We prove that Matthews and Baker’s Inverse Compositional Approach makes a smoothness assumption on the subspace basis that is equiva- lent to Hager and Belhumeur’s, which worsens convergence. Our approach differs from Hager and Belhumeur’s additive and Matthews and Baker’s compositional ap- proaches in that we make no smoothness assumptions on the subspace basis. In the experiments conducted we show that the model introduced accurately represents the appearance variations caused by illumination changes and facial expressions. We also verify experimentally that our fitting procedure is more accurate and has better convergence rate than the other related approaches, albeit at the expense of a slight increase in computational cost. Our approach can be used for tracking a human face at standard video frame rates on an average personal computer.

More information

Item ID: 1861
DC Identifier: http://oa.upm.es/1861/
OAI Identifier: oai:oa.upm.es:1861
Deposited by: profesor Luis Baumela Molina
Deposited on: 20 Oct 2009 07:13
Last Modified: 20 Apr 2016 07:03
  • Open Access
  • Open Access
  • Sherpa-Romeo
    Check whether the anglo-saxon journal in which you have published an article allows you to also publish it under open access.
  • Dulcinea
    Check whether the spanish journal in which you have published an article allows you to also publish it under open access.
  • Recolecta
  • e-ciencia
  • Observatorio I+D+i UPM
  • OpenCourseWare UPM