  Gabor kernels are widely accepted as dominant filters for iris recognition.
In this work we investigate, given the current interest in neural networks, if
Gabor kernels are the only family of functions performing best in iris
recognition, or if better filters can be learned directly from iris data. We
use (on purpose) a single-layer convolutional neural network as it mimics an
iris code-based algorithm. We learn two sets of data-driven kernels; one
starting from randomly initialized weights and the other from open-source set
of Gabor kernels. Through experimentation, we show that the network does not
converge on Gabor kernels, instead converging on a mix of edge detectors, blob
detectors and simple waves. In our experiments carried out with three
subject-disjoint datasets we found that the performance of these learned
kernels is comparable to the open-source Gabor kernels. These lead us to two
conclusions: (a) a family of functions offering optimal performance in iris
recognition is wider than Gabor kernels, and (b) we probably hit the maximum
performance for an iris coding algorithm that uses a single convolutional
layer, yet with multiple filters. Released with this work is a framework to
learn data-driven kernels that can be easily transplanted into open-source iris
recognition software (for instance, OSIRIS -- Open Source IRIS).
