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Research at St Andrews

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

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

Author(s)

Reza Shoja Ghiass, Oggie Arandelovic, Hakim Bendada, Xavier Maldague

School/Research organisations

Abstract

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 the y 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 the se 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.

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Details

Original languageEnglish
Title of host publicationProceedings of the 27th AAAI Conference on Artificial Intelligence, AAAI 2013
Pages357-364
Number of pages8
StatePublished - 2013
Event27th AAAI Conference on Artificial Intelligence, AAAI 2013 - Bellevue, WA, United States
Duration: 14 Jul 201318 Jul 2013

Conference

Conference27th AAAI Conference on Artificial Intelligence, AAAI 2013
CountryUnited States
CityBellevue, WA
Period14/07/1318/07/13

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ID: 242480483