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    Rights statement: This is the author’s version of a work that was accepted for publication in Optik. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Optik, 136, 2017 DOI: 10.1016/j.ijleo.2017.02.063

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On the estimation of face recognition system performance using image variability information

Research output: Contribution to journalJournal article

Published
<mark>Journal publication date</mark>05/2017
<mark>Journal</mark>Optik
Volume136
Number of pages14
Pages (from-to)619-632
Publication statusPublished
Early online date22/02/17
Original languageEnglish

Abstract

The type and amount of variation that exists among images in facial image datasets significantly affects Face Recognition System Performance (FRSP). This points towards the development of an appropriate image Variability Measure (VM), as applied to face-type image datasets. Given VM, modeling of the relationship that exists between the image variability characteristics of facial image datasets and expected FRSP values, can be performed. Thus, this paper presents a novel method to quantify the overall data variability that exists in a given face image dataset. The resulting Variability Measure (VM) is then used to model FR system performance versus VM (FRSP/VM). Note that VM takes into account both the inter- and intra-subject class correlation characteristics of an image dataset. Using eleven publically available datasets of face images and four well-known FR systems, computer simulation based experimental results showed that FRSP/VM based prediction errors are confined in the region of 0 to 10%.

Bibliographic note

This is the author’s version of a work that was accepted for publication in Optik. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Optik, 136, 2017 DOI: 10.1016/j.ijleo.2017.02.063