Similarity measurement construction is a key to fingerprint verification system. Highly accurate decision of genuine and impostor is directly inferred from a similarity score given by a sophisticated similarity measurement in the fingerprint matching process. This paper proposes a novel similarity measurement, which derives the likelihood ratio between two fingerprints with the consideration of the individual error rate instead of the average error rate of fingerprints. The proposed algorithm is also theoretically proved to be optimal for the minutiae-based fingerprint verification in terms of system error rates. Experimental results show that the proposed method has more efficient performance on separating genuine and impostor pairs.