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Ear Recognition Based on Improved Features Representations

  • Hakim DoghmaneEmail author
  • Hocine Bourouba
  • Kamel Messaoudi
  • El Bey Bournene
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 146)

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.

Keywords

Ear recognition Spatial pyramidal histogram (SPH) Multi bag of features KNN SVM 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Hakim Doghmane
    • 1
    Email author
  • Hocine Bourouba
    • 1
  • Kamel Messaoudi
    • 2
  • El Bey Bournene
    • 3
  1. 1.Université 8 Mai 1945 GuelmaGuelmaAlgeria
  2. 2.Mohamed Cherif Messaadia UniversitySouk-AhrasAlgeria
  3. 3.LE2I LaboratoryBurgundy UniversityDijonFrance

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