The paper is devoted to the application of machine learning methods for biometric identification of the iris. for biometric identification of a person by iris recognition. In order to investigate the possibilities of machine learning for the iris recognition, the UBIRIS database was used in this work. A distinctive feature of this database is that the task of identification is complicated by the registration of images in non-ideal conditions. This approach is aimed at developing algorithms for automatic iris recognition under limited conditions for obtaining high-quality images, remote registration, noise, and in the absence of cooperation by the subject. Glare from light sources, differences in lighting conditions, closure of part of the eye by the eyelids complicate the recognition process due to the significant fluctuation of features for different images of the iris of one person. The purpose of this work is to identify and compare the informative features of the iris obtained in different coordinate bases, as well as to choose the classification method that provides the highest accuracy in the identification of the person on the iris. Comparison of the use in iris recognition such features as the coefficients of two-dimensional discrete Fourier transform, the coefficients of discrete cosine transform, as well as the approximation coefficients of the 3rd and 4th levels of two-dimensional wavelet transform is considered. The accuracy of a number of classifiers using these feature sets is determined. Based on this, a combination of features and classifiers are proposed that provides the highest accuracy of person identification based on the iris recognition. The highest classification accuracy of the iris recognition was 98.5% using the approximation coefficients of the 4th level of two-dimensional wavelet decomposition and a classifier implementing the linear discriminant method. The use of two-dimensional discrete Fourier transform coefficients for iris recognition yielded 97.5% correctly identified individuals in the case of machine learning method based on the k-nearest neighbor ensemble. This algorithm also made it possible to obtain maximum classification accuracy (94.5%) for the coefficients of the discrete cosine transformation. The use of wavelet coefficients can significantly reduce the dimensionality of the data for classification compared to the use of Fourier transform coefficients or discrete cosine transform. Applying of vertical, horizontal and diagonal wavelet coefficients of detail as features for iris recognition gives lower accuracy compared to the use of wavelet coefficients of approximation of the same level.
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