Towards On-Device Face Recognition in Body-worn Cameras
This work addresses the need for efficient and private on-device face recognition in law enforcement and surveillance applications, though it is incremental as it applies existing lightweight models to a new dataset.
This study tackled the problem of enabling real-time face recognition on resource-constrained body-worn cameras by evaluating lightweight models like MobileNet-V2 and LightCNN-29 on the BWCface dataset, finding that LightCNN-29 achieved a maximum rank-1 accuracy only 1.85% lower than ResNet-50 while reducing parameters by 23.49M and being 2.1x faster on mobile devices.
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.