Utilizing motion data retrieval techniques for person identification

Jing Fu Juang, Wei Guang Teng

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

Abstract

Identifying a specific user is an old but challenging problem, and its applications are ubiquitous in our daily lives. Conventional person identification methods are using an ID card or the combination of a username and password. Recently, new techniques based on biometrics have been introduced so that people do not need to worry if they forget their username and password. For example, fingerprint and iris recognition are becoming common methods of person identification; however, users are usually required to interact with a system to use these traits. In some non-critical situations, it may be more convenient to utilize soft biometrics for person identification, although these features are not as unique for a specific person. In this work, we propose to conduct gait analysis that can be performed from a distance without disturbing user activities. We utilize depth cameras to capture user movements and create motion sequences. Then, a motion sequence is transformed to a motion string with appropriate data preprocessing and clustering techniques. Representative motion strings representing the individual behaviour of a user are retrieved and utilized to identify people. Empirical studies based on real motion data show that our approach performs well in person identification.

Original languageEnglish
Title of host publication2016 IEEE International Conference on Knowledge Engineering and Applications, ICKEA 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages188-192
Number of pages5
ISBN (Electronic)9781509034710
DOIs
Publication statusPublished - 2016 Dec 30
Event2016 IEEE International Conference on Knowledge Engineering and Applications, ICKEA 2016 - Singapore, Singapore
Duration: 2016 Sep 282016 Sep 30

Publication series

Name2016 IEEE International Conference on Knowledge Engineering and Applications, ICKEA 2016

Other

Other2016 IEEE International Conference on Knowledge Engineering and Applications, ICKEA 2016
CountrySingapore
CitySingapore
Period16-09-2816-09-30

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Information Systems
  • Artificial Intelligence
  • Computer Science Applications

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