  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.
