Several citizen service databases such as, police, national citizen identity, passport and vehicle registration, store both biographical and biometric information containing huge number of records. Achieving scalability and high accuracy for a 1:N person identification task on these databases is a huge challenge. In this work, we propose to use complementary information present in the biographical data along with biometric information of a user to improve 1:N person identification task for large systems. We show that a likelihood ratio based method for score level fusion of the biometric and biographical classifiers results in high accuracy identification as compared to using only the biometric classifiers or the biographical classifiers. © 2012 ICPR Org Committee.