Analysis of facial features in identical twins

Authors:
Brendan Klare;Alessandra A. Paulino;Anil K. Jain
Affiliations:
Dept. of Computer Science and Engineering, Michigan State University, East Lansing, U.S.A.;Dept. of Computer Science and Engineering, Michigan State University, East Lansing, U.S.A.;Dept. of Computer Science and Engineering, Michigan State University, East Lansing, U.S.A.
Venue:
IJCB '11 Proceedings of the 2011 International Joint Conference on Biometrics
Year:
2011

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Abstract

A study of the distinctiveness of different facial features (MLBP, SIFT, and facial marks) with respect to distinguishing identical twins is presented. The accuracy of distinguishing between identical twin pairs is measured using the entire face, as well as each facial component (eyes, eyebrows, nose, and mouth). The impact of discriminant learning methods on twin face recognition is investigated. Experimental results indicate that features that perform well in distinguishing identical twins are not always consistent with the features that best distinguish two non-twin faces.