Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Compact CNN Models for On-device Ocular-based User Recognition in Mobile
  Devices   A number of studies have demonstrated the efficacy of deep learning
convolutional neural network (CNN) models for ocular-based user recognition in
mobile devices. However, these high-performing networks have enormous space and
computational complexity due to the millions of parameters and computations
involved. These requirements make the deployment of deep learning models to
resource-constrained mobile devices challenging. To this end, only a handful of
studies based on knowledge distillation and patch-based models have been
proposed to obtain compact size CNN models for ocular recognition in the mobile
environment. In order to further advance the state-of-the-art, this study for
the first time evaluates five neural network pruning methods and compares them
with the knowledge distillation method for on-device CNN inference and mobile
user verification using ocular images. Subject-independent analysis on VISOB
and UPFR-Periocular datasets suggest the efficacy of layerwise magnitude-based
pruning at a compression rate of 8 for mobile ocular-based authentication using
ResNet50 as the base model. Further, comparison with the knowledge distillation
suggests the efficacy of knowledge distillation over pruning methods in terms
of verification accuracy and the real-time inference measured as deep feature
extraction time on five mobile devices, namely, iPhone 6, iPhone X, iPhone XR,
iPad Air 2 and iPad 7th Generation.

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The categories of this paper are: gr-qc
33
The categories of this paper are: plasm-ph
25
The categories of this paper are: patt-sol
36
The categories of this paper are: cmp-lg
24
The categories of this paper are: dg-ga
16
The categories of this paper are: stat
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The categories of this paper are: hep-ex
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The categories of this paper are: nucl-ex
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The categories of this paper are: supr-con
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The categories of this paper are: nucl-th
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The categories of this paper are: nlin
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The categories of this paper are: q-bio
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The categories of this paper are: adap-org
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The categories of this paper are: bayes-an
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The categories of this paper are: quant-ph
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The categories of this paper are: alg-geom