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

Khan, Muhammad Aurangzeb and Xydeas, Costas and Ahmed, Hassan (2017) On the estimation of face recognition system performance using image variability information. Optik, 136. pp. 619-632. ISSN 0030-4026

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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%.

Item Type:
Journal Article
Journal or Publication Title:
Optik
Additional Information:
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
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2200/2208
Subjects:
ID Code:
84846
Deposited By:
Deposited On:
24 Feb 2017 09:12
Refereed?:
Yes
Published?:
Published
Last Modified:
03 Jul 2020 02:56