Title: A novel discriminant multiscale representation for ear recognition

Authors: Hakim Doghmane; Abdelhani Boukrouche; Larbi Boubchir

Addresses: Laboratory of Inverse Problems, Modeling, Information and Systems (PIMIS), Department of Electronics and Telecommunications, Faculty of Sciences and Technology, Guelma University, Guelma, Algeria ' Laboratory of Inverse Problems, Modeling, Information and Systems (PIMIS), Department of Electronics and Telecommunications, Faculty of Sciences and Technology, Guelma University, Guelma, Algeria ' LIASD Research Lab., University of Paris 8, 2 Rue de la Liberté, 93526 Saint-Denis Cedex, France

Abstract: This paper proposes a novel representation for ear recognition. It introduces a new alternative of binarised statistical image features based on multiscale framework. The proposed representation allows capturing the image content at multiple resolutions. The recognition accuracy can be enhanced by the following steps. First, for a given ear image, a set of multiscale response images are derived from the bank of binarised statistical image features (B-BSIF) filter. Second, the obtained response images are summarised by concatenating their histograms, which are obtained at each scale. Finally, a discriminative ear image representation is build by projecting the above mentioned histograms into a linear discriminant analysis subspace. The proposed representation is applied on three public databases: IIT Delhi-1, IIT Delhi-2 and USTB. The obtained recognition accuracy confirms its performance than the recent existing methods.

Keywords: ear recognition; multi-resolution analysis; K-NN; whitened linear discriminant analysis; WLDA; B-BSIF.

DOI: 10.1504/IJBM.2019.096568

International Journal of Biometrics, 2019 Vol.11 No.1, pp.50 - 66

Received: 10 Oct 2017
Accepted: 13 Jul 2018
Published online: 05 Dec 2018 *

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