4 research outputs found
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
Complex-valued Iris Recognition Network
In this work, we design a 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 amplitude information
from the input iris texture in order to better represent its stochastic
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 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 automatic
complex-valued feature learning that is tailored for iris recognition.
Experiments conducted on three benchmark datasets - ND-CrossSensor-2013,
CASIA-Iris-Thousand and UBIRIS.v2 - show the benefit of the proposed network
for the task of iris recognition. Further, the generalization capability of the
proposed network is demonstrated by training and testing it across different
datasets. Finally, visualization schemes are used to convey the type of
features being extracted by the complex-valued network in comparison to
classical real-valued networks. The results of this work are likely to be
applicable in other domains, where complex Gabor filters are used for texture
modeling
Constrained Design of Deep Iris Networks
Despite the promise of recent deep neural networks to provide more accurate and efficient iris recognition compared to traditional techniques, 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 casts 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 optimal iris network architecture with the highest recognition accuracy 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 deep iris networks. It also enables us to learn an optimal iris network and demonstrate state-of-the-art performance with less computation and memory requirements. </p