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Identifying persons from iris images using neural networks for image segmentation and feature extraction
Yu.Kh. Ganeeva 2, E.V. Myasnikov 1,2

IPSI RAS – Branch of the FSRC "Crystallography and Photonics" RAS,
443001, Samara, Russia, Molodogvardeyskaya 151,
Samara National Research University, 443086, Samara, Russia, Moskovskoye Shosse 34

 PDF, 932 kB

DOI: 10.18287/2412-6179-CO-1023

Pages: 308-316.

Full text of article: Russian language.

The problem of personal identification plays an important role in information security. In recent years, biometric methods of personal identification have become most relevant and promising. The article presents a study of a method for identifying a person from iris images using a neural network approach at the stages of segmentation and a feature representation from the data. A description of a dataset used to implement the segmentation stage using convolutional neural networks is presented and access to the segmentation masks of the entire dataset is provided. A method is proposed for extracting a feature representation of the data using pretrained convolutional neural networks to solve a problem of iris classification. A comparative analysis of methods for extracting iris features, including classical approaches and a neural network approach, has been carried out. A comparative analysis of classification methods is carried out, including classical machine learning algorithms, namely, support vector machines, random forest, and a k-nearest neighbors method. The results of experimental studies have shown the high quality of the classification based on the proposed approach.

iris, identification, convolutional neural networks, image segmentation, recognition.

Ganeeva YK, Myasnikov EV. Identifying persons from iris images using neural networks for image segmentation and feature extraction. Computer Optics 2022; 46(2): 308-316. DOI: 10.18287/2412-6179-CO-1023.

This work was supported by the RF Ministry of Science and Higher Education within the State assignment of the FSRC "Crystallography and Photonics" RAS. The Experiments section uses the MMU Iris Database dataset provided by Multimedia University [43].


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