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.

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Biometric identification systems must be able to distin guish between individuals even in situations where the bio metric signature may be similar, such as in the case of iden tical twins. This paper presents experiments done in facial recognition using data from a set of images of twins. This work establishes the current state of facial recognition in regards to twins and the accuracy of current state-of-the art programs in distinguishing between identical twins us ing three commercial face matchers, Cognitec 8.3.2.0, Ver iLook 4.0, and PittPatt 4.2.1 and a baseline matcher em ploying Local Region PCA. Overall, it was observed that Cognitec had the best performance. All matchers, how ever, saw degradation in performance compared to an ex periment where the ability to distinguish unrelated persons was assessed. In particular, lighting and expression seemed to have affected performance the most.