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Vesselness features and the inverse compositional AAM for robust face recognition using thermal IR

Ghiass, Reza Shoja, Arandjelovic, Ognjen, Bendada, Hakim and Maldague, Xavier 2013, Vesselness features and the inverse compositional AAM for robust face recognition using thermal IR, in AAAI 2013 : Proceedings of the 27th AAAI Conference on Artificial Intelligence, AAAI Press, Palo Alto, Calif., pp. 357-364.

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Title Vesselness features and the inverse compositional AAM for robust face recognition using thermal IR
Author(s) Ghiass, Reza Shoja
Arandjelovic, Ognjen
Bendada, Hakim
Maldague, Xavier
Conference name Artificial Intelligence. AAAI Conference (27th : 2013 : Washington, D.C.)
Conference location Washington, D.C.
Conference dates 14-18 Jul. 2013
Title of proceedings AAAI 2013 : Proceedings of the 27th AAAI Conference on Artificial Intelligence
Editor(s) [Unknown]
Publication date 2013
Conference series AAAI Conference on Artificial Intelligence
Start page 357
End page 364
Total pages 8
Publisher AAAI Press
Place of publication Palo Alto, Calif.
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 the real world. While inherently insensitive to visible spectrum illumination changes, IR images introduce specific challenges of their 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, to normalize for pose and facial expression changes we generate a synthetic frontal image of a face in a canonical, neutral facial expression from an image of the face in an arbitrary pose and facial expression. This is achieved by piecewise affine warping which follows active appearance model (AAM) fitting. This is the first publication which explores the use of an AAM on thermal IR images; we propose a pre-processing step which enhances detail in thermal images, making AAM convergence faster and more accurate. To overcome the problem of thermal IR image sensitivity to the exact pattern of facial temperature emissions we describe a representation based on reliable anatomical features. In contrast to previous approaches, our representation is not binary; rather, our method accounts for the reliability of the extracted features. This makes the proposed representation much more robust both to pose and scale changes. The effectiveness of the proposed approach is demonstrated on the largest public database of thermal IR images of faces on which it achieved 100% identification rate, significantly outperforming previously described methods
Language swe
Field of Research 080104 Computer Vision
080106 Image Processing
080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
HERDC collection year 2013
Copyright notice ©2013, The Authors
Persistent URL http://hdl.handle.net/10536/DRO/DU:30057201

Document type: Conference Paper
Collection: Centre for Pattern Recognition and Data Analytics
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Created: Wed, 23 Oct 2013, 10:06:28 EST

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