Title
Double Trouble: Differentiating Identical Twins by Face Recognition
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.
Year
DOI
Venue
2014
10.1109/TIFS.2013.2296373
IEEE Transactions on Information Forensics and Security
Keywords
Field
DocType
face recognition
Computer vision,Facial recognition system,Biometrics access control,Pattern recognition,Three-dimensional face recognition,Computer science,Speech recognition,Artificial intelligence,Face detection
Journal
Volume
Issue
ISSN
9
2
1556-6013
Citations 
PageRank 
References 
4
0.42
6
Authors
9
Name
Order
Citations
PageRank
Jeffrey R. Paone191.26
Patrick J. Flynn24405307.04
P. Jonathon Phillips39209801.62
Kevin W. Bowyer411121734.33
Richard W. Vorder Bruegge51105.08
Patrick Grother6115892.33
George W. Quinn7523.22
Matthew T. Pruitt860.84
Jason M. Grant9171.90