Facial recognition is a key enabling component for emerging Internet of
Things (IoT) services such as smart homes or responsive offices. Through the
use of deep neural networks, facial recognition has achieved excellent
performance. However, this is only possibly when trained with hundreds of
images of each user in different viewing and lighting conditions. Clearly, this
level of effort in enrolment and labelling is impossible for wide-spread
deployment and adoption. Inspired by the fact that most people carry smart
wireless devices with them, e.g. smartphones, we propose to use this wireless
identifier as a supervisory label. This allows us to curate a dataset of facial
images that are unique to a certain domain e.g. a set of people in a particular
office. This custom corpus can then be used to finetune existing pre-trained
models e.g. FaceNet. However, due to the vagaries of wireless propagation in
buildings, the supervisory labels are noisy and weak.We propose a novel
technique, AutoTune, which learns and refines the association between a face
and wireless identifier over time, by increasing the inter-cluster separation
and minimizing the intra-cluster distance. Through extensive experiments with
multiple users on two sites, we demonstrate the ability of AutoTune to design
an environment-specific, continually evolving facial recognition system with
entirely no user effort.

本研究提出使用智能无线设备作为监督标签，通过 AutoTune 技术学习并完善人脸识别系统，实现无需用户干预的特定环境下不断演进的面部识别系统。