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Human ear recognition based on local multi-scale LBP features with city-block distance

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Abstract

The use of the ear as a biometric modality has emerged in recent years. It makes it possible to differentiate people thanks to its stability over time and to the richness of its characteristics such as texture, color and size. This paper proposes a novel approach to ear recognition based on a variant of the Local Binary Pattern descriptor called Multi-scale Local Binary Pattern (MLBP). MLBP is calculated locally, by dividing the image into several equal blocks, to extract the ear features which will be used in the matching process to make a decision by detecting the similarities between the feature vectors using City-Block distance (CTB). The proposed method is evaluated on three reference ear databases: IIT Delhi I, IIT Delhi II and USTB-1. The analysis of the results obtained have clearly shown the robustness and the stability of the proposed ear recognition method which is highly competitive, achieving an attractive recognition performances in terms of identification rate at rank-1 up to: 98.40% for IIT Delhi I, 98.64% for IIT Delhi II, and 98.33% for USTB-1.

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Notes

  1. Note that when the neighboring coordinate (ip,jp) does not correspond to integer values, the pixel value is estimated using bilinear interpolation.

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Authors and Affiliations

  1. PI:MIS Laboratory, University of 8 Mai 1945 Guelma, Guelma, Algeria

    Zineb Youbi & Abdelhani Boukrouche

  2. LIASD research Lab., University of Paris 8, 2 rue de la Liberté, 93526, Saint-Denis, France

    Larbi Boubchir

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  1. Zineb Youbi
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  2. Larbi Boubchir
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  3. Abdelhani Boukrouche
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Correspondence to Zineb Youbi.

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Youbi, Z., Boubchir, L. & Boukrouche, A. Human ear recognition based on local multi-scale LBP features with city-block distance. Multimed Tools Appl 78, 14425–14441 (2019). https://doi.org/10.1007/s11042-018-6768-9

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