A New performance evaluation method for face identification - regression analysis of misidentification risk

Wai-han Ho, Paul Watters

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

2 Citations (Scopus)
4 Downloads (Pure)

Abstract

The performance of a face identification system varies with its enrollment size. However, most experiments evaluated the performance of algorithms at only one enrollment size with the rank-1 identification rate. The current practice does not demonstrate the usability of algorithms thoroughly. But the problem is, in order to measure identification performance at different sizes, experimenters have to repeat the evaluation with samples of those sizes, which is almost impossible when they are large. Approaches using the Binomial theorem with match and non-match scores have been proposed to estimate performance at different sizes, but as a separate process from the evaluation itself. This paper presents a new way of evaluating identification algorithms that allows the estimating and comparing of performance at different sizes, using the regression analysis of Misidentification Risk.
Original languageEnglish
Title of host publicationProceedings of IEEE computer society conference on computer vision and pattern recognition (CVPR 2007)
EditorsPatrick Flynn
Place of PublicationMinneapolis, MN
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages6
ISBN (Print)1424411807
DOIs
Publication statusPublished - 2007
EventIEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2007) (25th : 2007) - Minneapolis, MN
Duration: 19 Jun 200721 Jun 2007

Conference

ConferenceIEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2007) (25th : 2007)
CityMinneapolis, MN
Period19/06/0721/06/07

Bibliographical note

Copyright 2007 IEEE. Reprinted from Proceedings of IEEE computer society conference on computer vision and pattern recognition (CVPR 2007). This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Macquarie University’s products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.

Keywords

  • binomial distribution
  • estimation theory
  • face recognition
  • performance evaluation
  • regression analysis

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