Paper Info
Title
A talking profile to distinguish identical twins
Abstract
Identical twins pose a great challenge to face recognition due to high similarities in their appearances. Motivated by the psychological findings that facial motion contains identity signatures and the observation that twins may look alike but behave differently, we develop a talking profile to use the identity signatures in the facial motion to distinguish between identical twins. The talking profile for a subject is defined as a collection of multiple types of usual face motions from the video. Given two talking profiles, we compute the similarities of all same type of face motion in both profiles and then perform the classification based on those similarities. Our approach, named Exceptional Motion Reporting Model (EMRM), is unrelated with appearance, and can handle realistic facial motion in human subjects, with no restrictions of speed of motion, or video frame rate. The experimental results on a video database containing 39 pairs of twins demonstrate that identical twins can be distinguished by their talking profiles. Moreover, we also apply our approach on non-twin population on a moderate YouTube dataset, with results verifying that the talking profile can be the potential biometric.
Year
DOI
Venue
2013
10.1109/FG.2013.6553700
Image Vision Comput.
Keywords
DocType
Volume
video signal processing,exceptional motion reporting model,face recognition,youtube dataset,emrm approach,image classification,biometric,identity signature,video frame rate,identical twin,face classification,facial motion,motion speed,talking profile,image motion analysis,psychology,databases,statistics,face,accuracy
Conference
32
Issue
ISSN
ISBN
10
2326-5396
978-1-4673-5544-5
Citations 
PageRank 
References 
1
0.35
8
Authors
6
Name
Order
Citations
PageRank
Li Zhang12286151.94
Hossein Nejati2325.29
Lewis Foo370.80
Keng Teck Ma4994.66
Dong Guo510.35
Terence Sim62562169.42