  Latent fingerprints are one of the most widely used forensic evidence by law
enforcement agencies. However, latent recognition performance is far from the
exemplary performance of sensor fingerprint recognition due to deformations and
artifacts within these images. In this study, we propose a fusion based local
matching approach towards latent fingerprint recognition. Recent latent
recognition studies typically relied on local descriptor generation methods, in
which either handcrafted minutiae features or deep neural network features are
extracted around a minutia of interest, in the latent recognition process.
Proposed approach would integrate these handcrafted features with a recently
proposed deep neural network embedding features in a multi-stage fusion
approach to significantly improve latent recognition results. Effectiveness of
the proposed approach has been shown on several public and private data sets.
As demonstrated in our experimental results, proposed method improves rank-1
identification accuracy by considerably for real-world datasets when compared
to either the single usage of these features or existing state-of-the-art
methods in the literature.
