Abstract
Abstract. Generation of a template containing spatial-frequency features of iris is an important stage of identification. The template is obtained by a wavelet transform in an image region specified by iris borders. One of the main characteristics of the identification system is the value of recognition error, equal error rate (EER) is used as criterion here. The optimal values (in sense of minimizing the EER) of wavelet transform parameters depend on many factors: image quality, sharpness, size of characteristic objects, etc. It is hard to isolate these factors and their influences. The work presents an attempt to study an influence of following factors to EER: iris segmentation precision, defocus level, noise level. Several public domain iris image databases were involved in experiments. The images were subjected to modelled distortions of said types. The dependencies of wavelet parameter and EER values from the distortion levels were build. It is observed that the increase of the segmentation error and image noise leads to the increase of the optimal wavelength of the wavelets, whereas the increase of defocus level leads to decreasing of this value.
Highlights
Adopted work-flow of identification of human by the iris image contains several steps: obtaining eye image, segmentation of the iris region, building the iris feature set, and matching two such sets by a distance function
One can see that optimal wavelength of Log-Gabor filter exhibit substantial decrease with the growth of blurring
Increasing of the noise predictably leads to increasing equal error rate (EER) and optimal wavelength
Summary
Adopted work-flow of identification of human by the iris image contains several steps: obtaining eye image, segmentation of the iris region, building the iris feature set, and matching two such sets by a distance function. Using inverted values S = 1/σ and W = 1/λ one can obtain simpler notation in frequency domain: GSW (u) = exp The modification of this is Log-Gabor function (Field, 1987), represented in frequency domain as:. In (Kumar and Passi, 2010) Log-Gabor filter is used, optimal parameters are determined for two databases for CASIA1 (Institute of Automation, Chinese Academy of Sciences, 2010) λ = 18, S′ = 0.55, for CASIA-3 λ = 22, S′ = 0.55. In this work the attempt is made to study how segmentation precision, degree of defocusing, noise level affects EER, λ and S′ optimal values.
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