3D ear shape reconstruction and recognition for biometric applications

Cho, Siu-Yeung (2013) 3D ear shape reconstruction and recognition for biometric applications. Signal, Image and Video Processing, 7 (4). pp. 609-618. ISSN 1863-1711

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This paper presents a new method based on a generalized neural reflectance (GNR) model for enhancing ear recognition under variations in illumination. It is based on training a number of synthesis images of each ear taken at single lighting direction with a single view. The way of synthesizing images can be used to build training cases for each ear under different known illumination conditions from which ear recognition can be significantly improved. Our training algorithm assigns to recognize the ear by similarity measure on ear features extracting firstly by the principal component analysis method and then further processing by the Fisher’s discriminant analysis to acquire lower-dimensional patterns. Experimental results conducted on our collected ear database show that lower error rates of individual and symmetry are achieved under different variations in lighting. The recognition performance of using our proposed GRN model significantly

outperforms the performance that without using the

proposed GNR model.

Item Type: Article
Additional Information: The final publication is available at Springer via http://dx.doi.org/10.1007/s11760-013-0481-y.
Keywords: Ear recognition; 3D shape reconstruction; Principal component analysis; Fisher’s discriminant analysis
Schools/Departments: University of Nottingham Ningbo China > Faculty of Science and Engineering > Department of Electrical and Electronic Engineering
Identification Number: https://doi.org/10.1007/s11760-013-0481-y
Depositing User: LIN, Zhiren
Date Deposited: 27 Sep 2017 11:46
Last Modified: 06 Jun 2018 10:23
URI: https://eprints.nottingham.ac.uk/id/eprint/46758

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