Image-set face recognition based on transductive learning

Mehrtash T. Harandi, Abbas Bigdeli, Brian C. Lovell

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

5 Citations (Scopus)

Abstract

In this paper we consider the problem of face recognition in a scenario when the query consists of a set of images and the gallery contains a single still image per subject. This is a more challenging problem compared to image-set to imageset matching and has wider applications in advanced surveillance, smart access control and human-computer interaction. Unfortunately most of the previous matching strategies in literature fail to work or deteriorate drastically if they are provided with one sample per class as the gallery data. In this paper we demonstrate how transductive learning can be utilized to map the image-set to single image matching problem into the recently-studied framework of set matching using canonical correlations. Experimental results on different challenging datasets reveal the efficiency of the proposed method against existing approaches.

Original languageEnglish
Title of host publication2010 IEEE International Conference on Image Processing, ICIP 2010 - Proceedings
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages2425-2428
Number of pages4
ISBN (Print)9781424479948
DOIs
Publication statusPublished - 1 Dec 2010
Externally publishedYes
EventIEEE International Conference on Image Processing 2010 - Hong Kong, China
Duration: 26 Sept 201029 Sept 2010
Conference number: 17th
https://ieeexplore.ieee.org/xpl/conhome/5641636/proceeding (Proceedings)

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880

Conference

ConferenceIEEE International Conference on Image Processing 2010
Abbreviated titleICIP 2010
Country/TerritoryChina
CityHong Kong
Period26/09/1029/09/10
Internet address

Keywords

  • And canonical correlation
  • Face recognition
  • Image-set matching
  • Transductive learning

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