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Constrained Design of Deep Iris Networks
Despite the promise of recent deep neural networks in the iris recognition
setting, there are vital properties of the classic IrisCode which are almost
unable to be achieved with current deep iris networks: the compactness of model
and the small number of computing operations (FLOPs). This paper re-models the
iris network design process as a constrained optimization problem which takes
model size and computation into account as learning criteria. On one hand, this
allows us to fully automate the network design process to search for the best
iris network confined to the computation and model compactness constraints. On
the other hand, it allows us to investigate the optimality of the classic
IrisCode and recent iris networks. It also allows us to learn an optimal iris
network and demonstrate state-of-the-art performance with less computation and
memory requirements
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