A Bayesian Method for Infrared Face Recognition

Chapter
Part of the Augmented Vision and Reality book series (Augment Vis Real, volume 1)

Abstract

In the context of face recognition, an important problem is accurate identification under variable illumination conditions. This problem has received relatively more attention in visible spectrum domain compared to the thermal infrared one. This was justified by both the higher cost of thermal sensors, the lack of widely available IR image databases and the quality of the produced images (lower resolution and higher image noise). Recently, thermal imagery of human faces has been established as a valid biometric signature and several approaches have been proposed, to tackle the problem of infrared face recognition, thanks to the advances of infrared imaging technology [Prokoski, Proceedings of the IEEE Workshop on Computer Vision Beyond the Visible Spectrum: Methods and Applications 5–14, 2000]. Some of these approaches have been based on machine learning techniques by supposing that the extracted infrared face features are Gaussian which is not generally an appropriate assumption. Motivated by the fact that infrared images are generally characterized by non-Gaussian features impossible to model using rigid distributions such as the Gaussian, we propose, in this chapter, an efficient Bayesian unsupervised algorithm for infrared face recognition, based on the Generalized Gaussian mixture model.

Keywords

Thermal infrared imaging Face recognition Edge direction Histogram Generalized Gaussian distribution Mixture modeling Bayesian analysis Metropolis-Hastings Gibbs sampling 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.Concordia Institute for Information Systems Engineering (CIISE)Concordia UniversityMontrealCanada

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