Latent Fingerprint Indexing: Robust Representation and Adaptive Candidate List

Efficiently identifying the mated gallery fingerprint of a latent fingerprint in a large database requires a highly accurate and efficient fingerprint matching algorithm. The common strategy to achieve this goal is to combine an efficient indexing algorithm with a slow but accurate matching algorithm. Despite of the importance of latent indexing, it has received far less attention than rolled and plain fingerprint indexing. Due to the small fingerprint area, poor image quality and huge variety in information quantity of latent fingerprints, existing rolled and plain fingerprint indexing approaches cannot be simply migrated to the latent fingerprint indexing. In this paper, we propose (1) a multi-scale fixed-length representation approach for latent fingerprint indexing, and (2) a fingerprint information quantity estimation approach for adaptive candidate list reduction. The representation scheme is designed to deal with small finger area and low image quality of latents. The information quantity of a latent is a predictor of the indexing score of its mated gallery fingerprint and thus can be used to determine a proper threshold for its candidate list. Extensive experimental results on NIST SD27, MOLF, N2N, and Hisign latent fingerprint databases show that the proposed method achieved the state-of-the-art indexing accuracy on latent fingerprints, and significantly improved the efficiency of state-of-the-art latent matching algorithm.