ReSPEcT: Privacy respecting thermal based specific person recognition

Ngai Seng Chan, Ka Ian Chan, Rita Tse, Su Kit Tang, Giovanni Pau

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)


Video analytic techniques have been used to extract high level information from video streams. The technique leverages advances on machine learning to summarize complex image data into simple alert-signal to attract the attention of human operators. For example, in a station for the underground video analytic can help the operator to focus on an event from a specific camera rather than leaving this only to the human eye. A concern of such techniques is privacy as they expose people identity and enable profiling of personal habits and orientations. This work introduces ReSPEcT (Privacy Respecting theRmal basEd Specific Person rECogniTion), a privacy preserving video analytic system based on thermal video streams. ReSPEcT is able to identify a specific-human in thermal video streams from low-cost, low resolution cameras. The system leverages recent advances in machine learning (CNNs) and a plethora of pre-processing mechanisms, such as image automatic labeling, image segmentation, and image augmentation to reduce the stream background noise, improve resilience, strengthen human-body classification, and finally enable a specific human-target identification. ReSPEcT’s automatic labeling tool significantly reduces time thus automatically performing labeling using a model that can be retrained by an interactive web application. The experimental evaluation shows that overall ReSPEcT achieve 96.83% accuracy in identifying a specific person. Furthermore, is important to notice that while ReSPEcT can identify a specific human, the tool is not aware of the real-identity as it operates only on thermal images. ReSPEcT paves the way to use video analytic in a variety of privacy-protected scenarios, such as confidential meetings, sensitive spaces, or even public toilets.

Original languageEnglish
Title of host publicationThirteenth International Conference on Digital Image Processing, ICDIP 2021
EditorsXudong Jiang, Hiroshi Fujita
ISBN (Electronic)9781510646001
Publication statusPublished - 2021
Event13th International Conference on Digital Image Processing, ICDIP 2021 - Singapore, Singapore
Duration: 20 May 202123 May 2021

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X


Conference13th International Conference on Digital Image Processing, ICDIP 2021


  • Ethical AI
  • Machine Learning
  • Privacy Preserving Computing
  • Thermal Video Analytic


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