Proceedings of International Conference on Applied Innovation in IT  ·  2026/06/12  ·  Vol. 14  ·  Issue 4  ·  pp. 169–175
An Algorithm for Person Recognition Based on Ear Images
Turdаli Jumаyev, Mаvludа Khоdjаevа, Dilmurоd Tuhtаnаzаrоv and Аnvаrjоn Mаhkаmоv
Currently, personal identification based on biometric parameters is one of the fastest growing areas. Systems developed based on biometric parameters are widely used in access control systems, electronic payments, forensics and a number of other areas. Therefore, the demand for personal identification systems based on biometric parameters is increasing day by day. Currently, we can cite biometric parameters such as facial images, fingerprints, ears, irises as examples of widely used biometric parameters. Methods and algorithms for identifying a person by ear images occupy an important place among biometric technologies. As it turned out, the accuracy and reliability of personal identification systems based on ear images are superior to other types of biometric technologies. Biometric technology based on personal identification based on this technology has several advantages over other biometric technologies. Firstly, unlike the human face, the ear practically does not change throughout a person’s life and depending on his mental state. Secondly, unlike biometric technologies based on the pupil of the eye, no complex devices other than a simple camera are required to obtain an image of the ear. From this it can be concluded that biometric technologies based on the image of the ear have good prospects. Nevertheless, the development of algorithms for identifying a person based on the image of the ear and its application in practical issues have not been sufficiently studied. Therefore, the issue of identifying a person based on the image of the ear is in the focus of attention of world scientists. The article proposes the use of a local binary character separation algorithm in combination with a Gabor filter in the development of a recognition system based on the image of the ear. This proposed work can be used in recognition systems based on biometric parameters.
Biometrics Biоmetriс Technologies Face Recognition Local Binary Pattern (LBP) Algorithm Ear Image Chi-Square Distance Cosine Similarity
References
  1. G. A. Kukharev, Biometric Systems: Methods and Tools for Identifying a Person’s Personality. St. Petersburg: Polytechnic, pp. 240, 2001.
  2. M. Burge and W. Burger, “Ear Biometrics,” in Biometrics: Personal Identification in a Networked Society, 1998, pp. 273-286.
  3. A. A. Mahkamov, T. S. Jumayev, D. S. Tukhtanazarov, and A. I. Dadamukhamedov, “Using AdaBoost to Improve the Performance of Simple Classifiers,” in Artificial Intelligence, Blockchain, Computing and Security, vol. 2, pp. 755-760, 2024.
  4. B. Moreno, Á. Sánchez, and J. F. Vélez, “On the Use of Outer Ear Images for Personal Identification in Security Applications,” in IEEE 33rd Annual International Carnahan Conference on Security Technology, 1999, pp. 469-476.
  5. T. Yuizono, Y. Wang, K. Satoh, and S. Nakayama, “Study on Individual Recognition for Ear Images by Using Genetic Local Search,” in Proceedings of the 2002 Congress on Evolutionary Computation, 2002, pp. 237-242.
  6. S. M. S. Islam, M. Bennamoun, A. S. Mian, and R. Davies, “A Fully Automatic Approach for Human Recognition from Profile Images Using 2D and 3D Ear Data,” in Proceedings of the Fourth International Symposium on 3D Data Processing, Visualization and Transmission (3DPVT’08), 2008, pp. 131-135.
  7. M. Rahman, M. R. Islam, N. I. Bhuiyan, B. Ahmed, and A. Islam, “Person Identification Using Ear Biometrics,” International Journal of the Computer, the Internet and Management, vol. 15, no. 2, pp. 1-8, 2007.
  8. B. Yang and S. Chen, “A Comparative Study on Local Binary Pattern (LBP) Based Face Recognition: LBP Histogram Versus LBP Image,” Neurocomputing, vol. 120, pp. 365-379, 2013.
  9. A. I. Khalil, M. M. Khalil, and S. H. Al-Khazraji, “A Biomedical Application Software for Measuring Particles Sizes in Electro-Microscopic Images,” Intellectual Technologies on Transport, no. 3, pp. 38-43, 2023.
  10. A. Khalil et al., “Microvascular Blood Flow with Laser Speckle Contrast Imaging: Analysis of Static Scatterers Effect Through Modelling and Simulation,” in 2014 European Modelling Symposium, IEEE, 2014.
  11. A. I. Khalil, M. S. Mohammed, and A. K. Abbas, “Processing of Brain Images Dataset: Introducing a Novel LBP Features Extraction Method to Enhance the Prediction System of Brain Hemorrhage,” JOIV: International Journal on Informatics Visualization, vol. 9, no. 4, pp. 1521-1529, 2025.
  12. A. Khalil, A. A. Al Wahaab, and A. Abbas, “A New Hybrid Metaheuristic Model for Image Edge Detection,” 2025.


Proceedings of the International Conference on Applied Innovations in IT by Anhalt University of Applied Sciences is licensed under CC BY-SA 4.0
 ·  This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License

ICAIIT 2026
International Conference on Applied Innovation in IT
Navigation
Publisher
ISSN2199-8876
Location Anhalt University of Applied Sciences
Phone +49 (0) 3496 67 5611
Address Building 01, Room 425
Bernburger Str. 55
D-06366 Köthen, Germany
Open Access License

All works are licensed under the Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0), unless otherwise noted.

Published by ICAIIT in cooperation with Anhalt University of Applied Sciences.

© 2026 ICAIIT — International Conference on Applied Innovations in IT. Anhalt University of Applied Sciences, Köthen, Germany.
Visitors: site traffic counter