Distinguishing Identical Twins by Face Recognition

Published

March 21, 2011

Author(s)

P J. Phillips, Patrick J. Flynn, Kevin W. Bowyer, Richard W. Vorder Bruegge, Patrick J. Grother, George W. Quinn, Matthew Pruitt

Abstract

The paper measures the ability of face recognition algorithms to distinguish between identical twin siblings. The experimental dataset consists of images taken of 126 pairs of identical twins (252 people) collected on the same day and 24 pairs of identical twins (48 people) with images collected one year apart. In terms of both the number of paris of twins and lapsed time between acquisitions, this is the most extensive investigation of face recognition performance on twins to date. Recognition experiments are conducted using three of the top submissions to the Multiple Biometric Evaluation (MBE) 2010 Still Face Track [1]. Performance results are reported for both same day and cross year matching. Performance results are broken out by lighting conditions (studio and outside); expression (neutral and smiling); gender and age. Confidence intervals were generated by a bootstrap method. This is the most detailed covariate analysis of face recognition of twins to date.

Proceedings Title

The Ninth IEEE International Conference on Automatic Face and Gesture Recognition (FG 2011)

Conference Dates

March 21-25, 2011

Conference Location

Santa Barbara, CA

Pub Type

Conferences

Citation

Phillips, P. , Flynn, P. , Bowyer, K. , Vorder, R. , Grother, P. , Quinn, G. and Pruitt, M. (2011), Distinguishing Identical Twins by Face Recognition, The Ninth IEEE International Conference on Automatic Face and Gesture Recognition (FG 2011), Santa Barbara, CA (Accessed December 10, 2024)