International Journal of Computational Intelligence Systems

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Volume 9, Issue 2, April 2016, Pages 296 - 310

Human Centric Recognition of 3D Ear Models

Authors
Guy De Tré1, Guy.DeTre@UGent.be, Robin De Mol1, Robin.DeMol@UGent.be, Dirk Vandermeulen2, dirk.vandermeulen@esat.kuleuven.be, Peter Claes2, peter.claes@esat.kuleuven.be, Jeroen Hermans2, jeroen.hermans@uzleuven.be, Joachim Nielandt1, Joachim.Nielandt@UGent.be
1Dept. of Telecommunications and Information Processing, Ghent University, Sint-Pietersnieuwstraat 41, B-9000 Ghent, Belgium
2Dept. of Electrical Engineering (ESAT), KU Leuven, Kasteelpark Arenberg 10, box 2440, B-3001 Leuven, Belgium
Received 15 September 2015, Accepted 8 January 2016, Available Online 1 April 2016.
DOI
10.1080/18756891.2016.1150002How to use a DOI?
Keywords
Ear photograph recognition; computational intelligence; bipolarity; aggregation
Abstract

Comparing ear photographs is considered to be an important aspect of disaster victim identification and other forensic and security applications. An interesting approach concerns the construction of 3D ear models by fitting the parameters of a ‘standard’ ear shape, in order to transform it into an optimal approximation of a 3D ear image. A feature list is then extracted from each 3D ear model and used in the recognition process. In this paper, we study how the quality and usability of a recognition process can be improved by computational intelligence techniques. More specifically, we study and illustrate how bipolar data modelling and aggregation techniques can be used for improving the representation and handling of data imperfections. A novel bipolar measure for computing the similarity between corresponding feature lists is proposed. This measure is based on the Minkowski distance, but explicitly deals with hesitation that is caused by bad image quality. Moreover, we investigate how forensic expert knowledge can be adequately reflected in the recognition process. For that reason, a hierarchically structured comparison technique for feature sets and other characteristics is proposed. Comparison results are expressed by bipolar satisfaction degrees and properly aggregated to an overall result. The benefits and added value of the novel technique are discussed and demonstrated by an illustrative example.

Copyright
© 2016. the authors. Co-published by Atlantis Press and Taylor & Francis
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

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<Previous Article In Issue
Next Article In Issue>
Journal
International Journal of Computational Intelligence Systems
Volume-Issue
9 - 2
Pages
296 - 310
Publication Date
2016/04/01
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
10.1080/18756891.2016.1150002How to use a DOI?
Copyright
© 2016. the authors. Co-published by Atlantis Press and Taylor & Francis
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

Cite this article

risenwbib
TY  - JOUR
AU  - Guy De Tré
AU  - Robin De Mol
AU  - Dirk Vandermeulen
AU  - Peter Claes
AU  - Jeroen Hermans
AU  - Joachim Nielandt
PY  - 2016
DA  - 2016/04/01
TI  - Human Centric Recognition of 3D Ear Models
JO  - International Journal of Computational Intelligence Systems
SP  - 296
EP  - 310
VL  - 9
IS  - 2
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2016.1150002
DO  - 10.1080/18756891.2016.1150002
ID  - DeTré2016
ER  -