Automatic Face Recognition Adaptation via Ambient Wireless Identifiers

Chris Xiaoxuan Lu, Peijun Zhao, Bowen Du, Hongkai Wen, Andrew Markham, Stefano Rosa, Niki Trigoni

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.
Original languageEnglish
Title of host publicationProceedings of the 16th ACM Conference on Embedded Networked Sensor Systems
Place of PublicationNew York, NY, USA
PublisherACM Association for Computing Machinery
Pages377–378
Number of pages2
ISBN (Print)9781450359528
DOIs
Publication statusPublished - 4 Nov 2018
Event16th ACM Conference on Embedded Networked Sensor Systems - Shenzhen, China
Duration: 4 Nov 20187 Nov 2018
http://sensys.acm.org/2018/

Publication series

NameSenSys '18
PublisherAssociation for Computing Machinery

Conference

Conference16th ACM Conference on Embedded Networked Sensor Systems
Abbreviated titleSenSys 2018
CountryChina
CityShenzhen
Period4/11/187/11/18
Internet address

Keywords

  • Face Recognition
  • Adaption of Learning Systems

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