Abstract
Abstract. In this article the new method for iris image features extraction based on phase congruency is proposed. Iris image key points are calculated using the convolutions with Hermite transform functions. At each key point the feature vector characterizing this key point is obtained based on the phase congruency method. Iris key point descriptor contains phase congruency values at points located on concentric circles around the key point. To compare the key points, Euclidean metric between the key points descriptors is calculated. The distance between the iris images is equal to the number of matched iris key points. The proposed method was tested using the images from CASIA−IrisV4−Interval database and the value of EER = 0.226% was obtained.
Highlights
Iris recognition is one of the most powerful biometric technologies for human identification
In this article we find the iris key points using Hermite transform functions and propose a new method for iris image features extraction based on phase congruency
At each iris key point feature vector contains phase congruency values at 24 points located on two concentric circles around the key point, where r x, x was used in iris key points extraction stage
Summary
Iris recognition is one of the most powerful biometric technologies for human identification. The most appealing feature of mobile devices is the wide availability and the presence of reliable sensors for capturing biometric traits, e.g., cameras and accelerometers. They more and more often store personal and sensitive data, that need to be protected. The feature extraction methods based on iris key points look promising to be used in iris recognition (Alvarez-Betancourt, 2016, Sahu, 2018). The iris key points matching methods can use the information about iris texture in local areas of key points to obtain more robust results. In this article we find the iris key points using Hermite transform functions and propose a new method for iris image features extraction based on phase congruency.
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