Ear description and recognition using ELBP and wavelets

The human ear is a new technology in biometrics which is not yet used in a real context or in commercial applications. For this purpose of biometric system, we present an improvement for ear recognition methods that use Elliptical Local Binary Pattern operator as a robust technique for characterizing the fine details of the two dimensional ear images. The improvements are focused on feature extraction and dimensionality reduction steps. The realized system is mainly appropriate for verification mode; it starts by decomposing the normalized ear image into several blocks with different resolutions. Next, the textural descriptor is applied on each decomposed block. A problem of information redundancies is appeared due to the important size of the concatenated histograms of all blocks, which has been resolved by reducing of the histogram's dimensionalities and by selecting of the pertinent information using Haar Wavelets. Finally, the system is evaluated on the IIT Delhi Database containing two dimensional ear images and we have obtained a success rate about 94% for 500 images from 100 persons.

[1]  Arun Ross,et al.  Handbook of Biometrics , 2007 .

[2]  Zhi-chun Mu,et al.  Ear Recognition based on 2D Images , 2007, 2007 First IEEE International Conference on Biometrics: Theory, Applications, and Systems.

[3]  Zhichun Mu,et al.  Ear recognition using LLE and IDLLE algorithm , 2008, 2008 19th International Conference on Pattern Recognition.

[4]  Ajay Kumar,et al.  Robust ear identification using sparse representation of local texture descriptors , 2013, Pattern Recognit..

[5]  Matti Pietikäinen,et al.  A comparative study of texture measures with classification based on featured distributions , 1996, Pattern Recognit..

[6]  Zhichun Mu,et al.  Ear recognition based on local information fusion , 2012, Pattern Recognit. Lett..

[7]  Abdelhani Boukrouche,et al.  System for automatic faces detection , 2012, 2012 3rd International Conference on Image Processing Theory, Tools and Applications (IPTA).

[8]  Banafshe Arbab-Zavar,et al.  On guided model-based analysis for ear biometrics , 2011, Comput. Vis. Image Underst..

[9]  Ajit D Dinkar,et al.  Person identification in Ethnic Indian Goans using ear biometrics and neural networks. , 2012, Forensic science international.

[10]  Johannes Kepler,et al.  Ear Biometrics for Machine Vision , 2001 .

[11]  M.S. Nixon,et al.  On Model-Based Analysis of Ear Biometrics , 2007, 2007 First IEEE International Conference on Biometrics: Theory, Applications, and Systems.

[12]  Wilhelm Burger,et al.  Ear biometrics in computer vision , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[13]  Alice Caplier,et al.  Elliptical Local Binary Patterns for Face Recognition , 2012, ACCV Workshops.

[14]  Loris Nanni,et al.  Fusion of color spaces for ear authentication , 2009, Pattern Recognit..

[15]  Chenye Wu,et al.  Automated human identification using ear imaging , 2012, Pattern Recognit..