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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Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
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 -