Latent Fingerprint Matching via Dense Minutia Descriptor
This addresses the challenge of matching low-quality latent fingerprints for forensic and security applications, representing an incremental improvement over existing methods.
The authors tackled the problem of latent fingerprint matching, which is difficult due to poor quality fingerprints, by proposing a deep-learning based dense minutia descriptor (DMD) that captures detailed minutia and texture information, achieving state-of-the-art performance on several datasets.
Latent fingerprint matching is a daunting task, primarily due to the poor quality of latent fingerprints. In this study, we propose a deep-learning based dense minutia descriptor (DMD) for latent fingerprint matching. A DMD is obtained by extracting the fingerprint patch aligned by its central minutia, capturing detailed minutia information and texture information. Our dense descriptor takes the form of a three-dimensional representation, with two dimensions associated with the original image plane and the other dimension representing the abstract features. Additionally, the extraction process outputs the fingerprint segmentation map, ensuring that the descriptor is only valid in the foreground region. The matching between two descriptors occurs in their overlapping regions, with a score normalization strategy to reduce the impact brought by the differences outside the valid area. Our descriptor achieves state-of-the-art performance on several latent fingerprint datasets. Overall, our DMD is more representative and interpretable compared to previous methods.