Iris Image Key Points Descriptors Based on Phase Congruency

Abstract

<p><strong>Abstract.</strong> 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.</p>

Fractional Wavelet Transform Phase for Iris Image Key Points Matching

“…For the normalized iris image the iris key points are found using the Hermite functions (Pavelyeva, 2013, Protsenko, 2019. Then we construct the iris key points descriptors using the fractional phase congruency.…”

“…For two-dimensional case the log-Gabor wavelet function is used, and the two-dimensional wavelet transform is calculated using the convolution theorem. The taken parameters of log-Gabor function are described in(Protsenko, 2019). Six orientations and four scales of log-Gabor functions are taken.…”

Image Key Point Matching by Phase Congruency

“…The iris-image key points are detected by convolution of the normalized image intensity function with the Hermite transform [28]. Then key-point descriptors are matched to find equal features in the biometric images of the iris [42] (Fig. 6).…”