Towards Self-Supervised Face Labeling via Cross-Modality Association

Chris Xiaoxuan Lu, Xuan Kan, Stefano Rosa, Bowen Du, Hongkai Wen, Andrew Markham, Niki Trigoni

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


Face recognition has become the de facto authentication solution in a broad spectrum of applications, from smart buildings, to industrial monitoring and security services. However, in many of those real-world scenarios, tracking or identifying people with facial recognition is extremely challenging due to the variations in the environment such as lighting conditions, camera viewing angles and subject motion. For most of the state-of-the-art face recognition systems, they need to be trained on a large dataset containing a good variety of labelled face images to work well. However, collecting and manually labelling such datasets is difficult and time consuming, probably more so than developing the algorithms. In this paper, we propose a novel framework to automatically label user identities with their face images in smart spaces, exploiting the fact that the users tend to carry their smart devices while seen by the surveillance cameras. We evaluate our method on 10 users in a smart building setting, and the experimental results show that our method can achieve > 0.9 f1 score on average.
Original languageEnglish
Title of host publicationProceedings of the 15th ACM Conference on Embedded Network Sensor Systems
Place of PublicationNew York, NY, USA
PublisherACM Association for Computing Machinery
Number of pages2
ISBN (Print)9781450354592
Publication statusPublished - 6 Nov 2017
Event15th ACM Conference on Embedded Networked Sensor Systems -, Delft, Netherlands
Duration: 5 Nov 20178 Nov 2017

Publication series

NameSenSys '17
PublisherAssociation for Computing Machinery


Conference15th ACM Conference on Embedded Networked Sensor Systems
Abbreviated titleSenSys 2015


  • Cross-modality Association
  • WiFi
  • Camera


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