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

Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.14/25811

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Title: A New performance evaluation method for face identification - regression analysis of misidentification risk
Related: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2007) (25th : 2007) (19 - 21 June 2007 : Minneapolis, MN)
Related: Flynn, Patrick. Proceedings of IEEE computer society conference on computer vision and pattern recognition (CVPR 2007)
DOI: 10.1109/CVPR.2007.383276
Publisher: Minneapolis, MN : IEEE
Date: 2007
Author/Creator: Ho, Wai-han
Author/Creator: Watters, Paul
Description: 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.
Description: 6 page(s)
Subject Keyword: binomial distribution
Subject Keyword: estimation theory
Subject Keyword: face recognition
Subject Keyword: performance evaluation
Subject Keyword: regression analysis
Resource Type: conference paper
Organisation: Macquarie University. Dept. of Computing

Identifier: http://hdl.handle.net/1959.14/25811
Identifier: ISBN:1424411807
Identifier: mq-rm-2007003206
Language: eng
Rights: 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.
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