  In this work, we design a fully complex-valued neural network for the task of
iris recognition. Unlike the problem of general object recognition, where
real-valued neural networks can be used to extract pertinent features, iris
recognition depends on the extraction of both phase and magnitude information
from the input iris texture in order to better represent its biometric content.
This necessitates the extraction and processing of phase information that
cannot be effectively handled by a real-valued neural network. In this regard,
we design a fully complex-valued neural network that can better capture the
multi-scale, multi-resolution, and multi-orientation phase and amplitude
features of the iris texture. We show a strong correspondence of the proposed
complex-valued iris recognition network with Gabor wavelets that are used to
generate the classical IrisCode; however, the proposed method enables a new
capability of automatic complex-valued feature learning that is tailored for
iris recognition. We conduct experiments on three benchmark datasets -
ND-CrossSensor-2013, CASIA-Iris-Thousand and UBIRIS.v2 - and show the benefit
of the proposed network for the task of iris recognition. We exploit
visualization schemes to convey how the complex-valued network, when compared
to standard real-valued networks, extracts fundamentally different features
from the iris texture.
