Pilot Iris Recognition Based on Spherical Haar Wavelet and Convolutional Neural Network

Iris recognition faces two important issues. they are how to decompose finely and reconstruct the spherical image of the iris, and how to identify the characteristics of the iris. Conventional iris recognition uses usually the planar features of these iris images. However, the human eye is a sphere. The geometric position information of the iris surface is an important signal, but it is difficult to extract the geometric features of the iris sphere from the planar image. Considering the issue that the plane features are prone to distortion and lack fidelity of iris texture, an Orthogonal and Symmetric Spherical Haar Wavelet (OSSHW) basis is proposed to decompose and reconstruct the spherical iris signal to obtain stronger geometric features of iris surface. The comparison of the feature extraction ability to spherical signal by the spherical harmonics and the typical semi-orthogonal or nearly orthogonal spherical Haar wavelet is also presented. And then, an iris recognition method based on Convolutional Neural Networks (CNN) + OSSHW is proposed, which can effectively capture the local fine features of iris spherical surface, and has stronger ability in iris recognition than semi-orthogonal or nearly orthogonal spherical Haar wavelet bases.