Face recognition from a single registered image for conference socializing

Yu Zhao, Yan Liu, Yang Liu, Shenghua Zhong, Kien A. Hua

Research output: Journal article publicationJournal articleAcademic researchpeer-review

10 Citations (Scopus)

Abstract

Scientific conferences are primary venues for connecting with and forming relationships with fellow researchers and scientists. Thus, over the course of a conference participants often take advantage of the many opportunities to network. In this setting, it is desirable to quickly recognize the identity of the persons we see and wish to meet. In particular, it could be embarrassing to not recognize a prominent researcher. In this paper, we investigate a novel face recognition framework that is applicable to conference socialization scenarios. In the proposed framework, only frontal images are used as training images; and face recognition is possible from an arbitrary view of a subject. Our system prototype assumes that the conference participants have uploaded a frontal photo during the registration process. At the conference, the identity of a person can be recognized from a picture, taken from an arbitrary angle with a standard mobile phone. Our experimental results indicate that the proposed framework is robust to possible large pose variations between the non-frontal image captured impromptu and the training image of the same person. Experiments based upon standard face dataset and real conference socializing datasets are conducted to test the effectiveness of the proposed techniques.
Original languageEnglish
Pages (from-to)973-979
Number of pages7
JournalExpert Systems with Applications
Volume42
Issue number3
DOIs
Publication statusPublished - 15 Feb 2015

Keywords

  • Conference socializing
  • Face recognition
  • Large pose variation
  • Single registered image

ASJC Scopus subject areas

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

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