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.
G. A. Kukharev, Biometric Systems: Methods and Tools for Identifying a Person’s Personality. St. Petersburg: Polytechnic, pp. 240, 2001.
M. Burge and W. Burger, “Ear Biometrics,” in Biometrics: Personal Identification in a Networked Society, 1998, pp. 273-286.
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.
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.
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.
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.
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.
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.
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.
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.
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.
A. Khalil, A. A. Al Wahaab, and A. Abbas, “A New Hybrid Metaheuristic Model for Image Edge Detection,” 2025.