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Ear Recognition Based on Improved Features Representations

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Proceedings of the 8th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT’18), Vol.1 (SETIT 2018)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 146))

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Abstract

This paper presents an ear recognition framework based on an improved representation of multi-bag of visual features model, which relies on significantly Binarized Statistical Image Features, clustering algorithm and Spatial Pyramid Histogram decomposition method. The following steps can enhance the recognition accuracy. Firstly, the Binarized Statistical Image Features is used to capture the texture information in ear image. Secondly, the multi bag-of visual features dictionary is learned from the training image responses in the feature space, using K-means algorithm. Thirdly, the spatial pyramid histogram of horizontal decomposition is applied to obtain local ear feature descriptors. Next, the histograms obtained are normalized. Then, the global representation of the ear image is obtained by concatenating all histograms calculated at each level. After that, the discriminant representation of ear image is constructed, using kernel Fisher discriminant analysis. Finally, the k-nearest neighbor and the support vector machine classifiers are used for ear identification. The experimental results achieve average rank-1 recognition accuracy of 97.81%, 97.91% and 99.20%, respectively, in the IIT-Delhi-1, IIT-Delhi-2 and USTB-1 publicly available database. This shows that the proposed approach provides a significant improvement performance over the state-of-the-art in terms of accuracy.

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Author information

Authors and Affiliations

  1. Université 8 Mai 1945 Guelma, BP. 401, 24000, Guelma, Algeria

    Hakim Doghmane & Hocine Bourouba

  2. Mohamed Cherif Messaadia University, 41000, Souk-Ahras, Algeria

    Kamel Messaoudi

  3. LE2I Laboratory, Burgundy University, BP 47 870, Dijon, France

    El Bey Bournene

Authors
  1. Hakim Doghmane
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  2. Hocine Bourouba
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  3. Kamel Messaoudi
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  4. El Bey Bournene
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Corresponding author

Correspondence to Hakim Doghmane .

Editor information

Editors and Affiliations

  1. SETIT Lab, University of Sfax, Sfax, Tunisia

    Med Salim Bouhlel

  2. DIBRIS - University of Genoa, Genova, Genova, Italy

    Stefano Rovetta

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Doghmane, H., Bourouba, H., Messaoudi, K., Bournene, E.B. (2020). Ear Recognition Based on Improved Features Representations. In: Bouhlel, M., Rovetta, S. (eds) Proceedings of the 8th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT’18), Vol.1. SETIT 2018. Smart Innovation, Systems and Technologies, vol 146. Springer, Cham. https://doi.org/10.1007/978-3-030-21005-2_24

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