Double Trouble: Differentiating Identical Twins by Face Recognition

Author

Paone, Jeffrey R. ; Flynn, Patrick J. ; Philips, P. Jonathon ; Bowyer, Kevin W. ; Bruegge, Richard W. Vorder ; Grother, Patrick J. ; Quinn, George W. ; Pruitt, Matthew T. ; Grant, Jason M.

Author_Institution

Dept. of Comput. Sci. & Eng., Univ. of Notre Dame, Notre Dame, IN, USA

Volume

9

Issue

2

fYear

2014

fDate

Feb. 2014

Firstpage

285

Lastpage

295

Abstract

Facial recognition algorithms should be able to operate even when similar-looking individuals are encountered, or even in the extreme case of identical twins. An experimental data set comprised of 17486 images from 126 pairs of identical twins (252 subjects) collected on the same day and 6864 images from 120 pairs of identical twins (240 subjects) with images taken a year later was used to measure the performance on seven different face recognition algorithms. Performance is reported for variations in illumination, expression, gender, and age for both the same day and cross-year image sets. Regardless of the conditions of image acquisition, distinguishing identical twins are significantly harder than distinguishing subjects who are not identical twins for all algorithms.

Keywords

biometrics (access control); face recognition; face recognition; gesture recognition; identical twins; Algorithm design and analysis; Cameras; Face; Face recognition; Lighting; Sociology; Statistics; Face and gesture recognition;

fLanguage

English

Journal_Title

Information Forensics and Security, IEEE Transactions on

Publisher

ieee

ISSN

1556-6013

Type

jour

DOI

10.1109/TIFS.2013.2296373

Filename

6693698