Home > Research > Publications & Outputs > On the estimation of face recognition system pe...

Electronic data

  • 1-s2.0-S0030402617302061-main

    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

    Accepted author manuscript, 606 KB, PDF document

    Available under license: CC BY-NC-ND: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License

Links

Text available via DOI:

View graph of relations

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

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Published

Standard

On the estimation of face recognition system performance using image variability information. / Khan, Muhammad Aurangzeb; Xydeas, Costas; Ahmed, Hassan.
In: Optik, Vol. 136, 05.2017, p. 619-632.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

APA

Vancouver

Khan MA, Xydeas C, Ahmed H. On the estimation of face recognition system performance using image variability information. Optik. 2017 May;136:619-632. Epub 2017 Feb 22. doi: 10.1016/j.ijleo.2017.02.063

Author

Bibtex

@article{ed6ee76d2c7a4dccb9cb2122a8ad340a,
title = "On the estimation of face recognition system performance using image variability information",
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%.",
keywords = "Face recognition (FR), Signal Variability in image face datasets, Facial Variability Measure and its relationship to FR performance",
author = "Khan, {Muhammad Aurangzeb} and Costas Xydeas and Hassan Ahmed",
note = "This is the author{\textquoteright}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",
year = "2017",
month = may,
doi = "10.1016/j.ijleo.2017.02.063",
language = "English",
volume = "136",
pages = "619--632",
journal = "Optik",
issn = "0030-4026",
publisher = "Urban und Fischer Verlag Jena",

}

RIS

TY - JOUR

T1 - On the estimation of face recognition system performance using image variability information

AU - Khan, Muhammad Aurangzeb

AU - Xydeas, Costas

AU - Ahmed, Hassan

N1 - 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

PY - 2017/5

Y1 - 2017/5

N2 - 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%.

AB - 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%.

KW - Face recognition (FR)

KW - Signal Variability in image face datasets

KW - Facial Variability Measure and its relationship to FR performance

U2 - 10.1016/j.ijleo.2017.02.063

DO - 10.1016/j.ijleo.2017.02.063

M3 - Journal article

VL - 136

SP - 619

EP - 632

JO - Optik

JF - Optik

SN - 0030-4026

ER -