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Illumination-invariant face recognition from a single image across extreme pose using a dual dimension AAM ensemble in the thermal infrared spectrum

Ghiass, Reza Shoja, Arandjelovic, Ognjen, Bendada, Hakim and Maldague, Xavier 2013, Illumination-invariant face recognition from a single image across extreme pose using a dual dimension AAM ensemble in the thermal infrared spectrum, in IJCNN 2013 : Proceedings of the International Joint Conference on Neural Networks, IEEE, Piscataway, N.J., pp. 2781-2790, doi: 10.1109/IJCNN.2013.6707095.

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Title Illumination-invariant face recognition from a single image across extreme pose using a dual dimension AAM ensemble in the thermal infrared spectrum
Author(s) Ghiass, Reza Shoja
Arandjelovic, Ognjen
Bendada, Hakim
Maldague, Xavier
Conference name International Joint Conference on Neural Networks (2013 : Dallas, Texas)
Conference location Dallas, Texas
Conference dates 4-9 Aug. 2013
Title of proceedings IJCNN 2013 : Proceedings of the International Joint Conference on Neural Networks
Editor(s) [Unknown]
Publication date 2013
Conference series International Joint Conference on Neural Networks
Start page 2781
End page 2790
Total pages 10
Publisher IEEE
Place of publication Piscataway, N.J.
Summary Over the course of the last decade, infrared (IR) and particularly thermal IR imaging based face recognition has emerged as a promising complement to conventional, visible spectrum based approaches which continue to struggle when applied in practice. While inherently insensitive to visible spectrum illumination changes, IR data introduces specific challenges of its own, most notably sensitivity to factors which affect facial heat emission patterns, e.g. emotional state, ambient temperature, and alcohol intake. In addition, facial expression and pose changes are more difficult to correct in IR images because they are less rich in high frequency detail which is an important cue for fitting any deformable model. In this paper we describe a novel method which addresses these major challenges. Specifically, when comparing two thermal IR images of faces, we mutually normalize their poses and facial expressions by using an active appearance model (AAM) to generate synthetic images of the two faces with a neutral facial expression and in the same view (the average of the two input views). This is achieved by piecewise affine warping which follows AAM fitting. A major contribution of our work is the use of an AAM ensemble in which each AAM is specialized to a particular range of poses and a particular region of the thermal IR face space. Combined with the contributions from our previous work which addressed the problem of reliable AAM fitting in the thermal IR spectrum, and the development of a person-specific representation robust to transient changes in the pattern of facial temperature emissions, the proposed ensemble framework accurately matches faces across the full range of yaw from frontal to profile, even in the presence of scale variation (e.g. due to the varying distance of a subject from the camera). The effectiveness of the proposed approach is demonstrated on the largest public database of thermal IR images of faces and a newly acquired data set of thermal IR motion videos. Our approach achieved perfect recognition performance on both data sets, significantly outperforming the current state of the art methods even when they are trained with multiple images spanning a range of head views.
ISBN 9781467361293
Language eng
DOI 10.1109/IJCNN.2013.6707095
Field of Research 080104 Computer Vision
080106 Image Processing
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2013, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30057176

Document type: Conference Paper
Collection: Centre for Pattern Recognition and Data Analytics
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