Latent Fingerprint Recognition: Role of Texture Template
This work addresses the challenge of recognizing poor-quality latent fingerprints for forensic and security applications, representing an incremental improvement over existing methods.
The paper tackled the problem of low accuracy and high computational cost in latent fingerprint recognition by proposing a texture template approach with virtual minutiae, achieving an 8.9% improvement in rank-1 accuracy and reducing matching time from 11 ms to 7.7 ms per comparison on the NIST SD27 database.
We propose a texture template approach, consisting of a set of virtual minutiae, to improve the overall latent fingerprint recognition accuracy. To compensate for the lack of sufficient number of minutiae in poor quality latent prints, we generate a set of virtual minutiae. However, due to a large number of these regularly placed virtual minutiae, texture based template matching has a large computational requirement compared to matching true minutiae templates. To improve both the accuracy and efficiency of the texture template matching, we investigate: i) both original and enhanced fingerprint patches for training convolutional neural networks (ConvNets) to improve the distinctiveness of descriptors associated with each virtual minutiae, ii) smaller patches around virtual minutiae and a fast ConvNet architecture to speed up descriptor extraction, iii) reduce the descriptor length, iv) a modified hierarchical graph matching strategy to improve the matching speed, and v) extraction of multiple texture templates to boost the performance. Experiments on NIST SD27 latent database show that the above strategies can improve the matching speed from 11 ms (24 threads) per comparison (between a latent and a reference print) to only 7.7 ms (single thread) per comparison while improving the rank-1 accuracy by 8.9% against 10K gallery.