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Gabor kernels are widely accepted as dominant filters for iris recognition.In this work we investigate, given the current interest in neural networks, ifGabor kernels are the only family of functions performing best in irisrecognition, or if better filters can be learned directly from iris data. Weuse (on purpose) a single-layer convolutional neural network as it mimics aniris code-based algorithm. We learn two sets of data-driven kernels; onestarting from randomly initialized weights and the other from open-source setof Gabor kernels. Through experimentation, we show that the network does notconverge on Gabor kernels, instead converging on a mix of edge detectors, blobdetectors and simple waves. In our experiments carried out with threesubject-disjoint datasets we found that the performance of these learnedkernels is comparable to the open-source Gabor kernels. These lead us to twoconclusions: (a) a family of functions offering optimal performance in irisrecognition is wider than Gabor kernels, and (b) we probably hit the maximumperformance for an iris coding algorithm that uses a single convolutionallayer, yet with multiple filters. Released with this work is a framework tolearn data-driven kernels that can be easily transplanted into open-source irisrecognition software (for instance, OSIRIS -- Open Source IRIS).

Are Gabor Kernels Optimal for Iris Recognition?