Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Towards On-Device Face Recognition in Body-worn Cameras   Face recognition technology related to recognizing identities is widely
adopted in intelligence gathering, law enforcement, surveillance, and consumer
applications. Recently, this technology has been ported to smartphones and
body-worn cameras (BWC). Face recognition technology in body-worn cameras is
used for surveillance, situational awareness, and keeping the officer safe.
Only a handful of academic studies exist in face recognition using the
body-worn camera. A recent study has assembled BWCFace facial image dataset
acquired using a body-worn camera and evaluated the ResNet-50 model for face
identification. However, for real-time inference in resource constraint
body-worn cameras and privacy concerns involving facial images, on-device face
recognition is required. To this end, this study evaluates lightweight
MobileNet-V2, EfficientNet-B0, LightCNN-9 and LightCNN-29 models for face
identification using body-worn camera. Experiments are performed on a publicly
available BWCface dataset. The real-time inference is evaluated on three mobile
devices. The comparative analysis is done with heavy-weight VGG-16 and
ResNet-50 models along with six hand-crafted features to evaluate the trade-off
between the performance and model size. Experimental results suggest the
difference in maximum rank-1 accuracy of lightweight LightCNN-29 over
best-performing ResNet-50 is \textbf{1.85\%} and the reduction in model
parameters is \textbf{23.49M}. Most of the deep models obtained similar
performances at rank-5 and rank-10. The inference time of LightCNNs is 2.1x
faster than other models on mobile devices. The least performance difference of
\textbf{14\%} is noted between LightCNN-29 and Local Phase Quantization (LPQ)
descriptor at rank-1. In most of the experimental settings, lightweight
LightCNN models offered the best trade-off between accuracy and the model size
in comparison to most of the models.

20
The categories of this paper are: acc-phys
19
The categories of this paper are: eess
29
The categories of this paper are: funct-an
21
The categories of this paper are: adap-org
9
The categories of this paper are: nlin
11
The categories of this paper are: hep-lat
35
The categories of this paper are: mtrl-th
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The categories of this paper are: bayes-an
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The categories of this paper are: nucl-ex
33
The categories of this paper are: plasm-ph
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The categories of this paper are: physics
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The categories of this paper are: nucl-th
5
The categories of this paper are: gr-qc
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The categories of this paper are: patt-sol
37
The categories of this paper are: supr-con
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The categories of this paper are: stat