Automatic Face Recognition Adaptation via Ambient Wireless Identifiers

Abstract

Face recognition is a key enabling service for smart-spaces, allowing building management agents to easily monitor 'who is where', anticipating user needs and tailoring their local environment and experiences. Although facial recognition, especially through the use of deep neural networks, has achieved stellar performance over large datasets, the majority of approaches require supervised learning, that is, to be trained with tens or hundreds of images of users in different poses and lighting conditions. In this paper, we motivate that this enrollment effort is unnecessary if the smart-space has access to a wireless identifier e.g., through a smart-phone's MAC address. By learning and refining the noisy and weak association between a user's smart-phone and facial images, AutoTune can fine-tune a deep neural network to tailor it to the environment, users and conditions of a particular camera or set of cameras.