  Latent fingerprints are usually processed with Automated Fingerprint
Identification Systems (AFIS) by law enforcement agencies to narrow down
possible suspects from a criminal database. AFIS do not commonly use all
discriminatory features available in fingerprints but typically use only some
types of features automatically extracted by a feature extraction algorithm. In
this work, we explore ways to improve rank identification accuracies of AFIS
when only a partial latent fingerprint is available. Towards solving this
challenge, we propose a method that exploits extended fingerprint features
(unusual/rare minutiae) not commonly considered in AFIS. This new method can be
combined with any existing minutiae-based matcher. We first compute a
similarity score based on least squares between latent and tenprint minutiae
points, with rare minutiae features as reference points. Then the similarity
score of the reference minutiae-based matcher at hand is modified based on a
fitting error from the least square similarity stage. We use a realistic
forensic fingerprint casework database in our experiments which contains rare
minutiae features obtained from Guardia Civil, the Spanish law enforcement
agency. Experiments are conducted using three minutiae-based matchers as a
reference, namely: NIST-Bozorth3, VeriFinger-SDK and MCC-SDK. We report
significant improvements in the rank identification accuracies when these
minutiae matchers are augmented with our proposed algorithm based on rare
minutiae features.
