Latent fingerprint minutia extraction using fully convolutional network
This work addresses the challenge of fingerprint identification for latent prints, which are often poor in quality, by improving minutia extraction accuracy and speed, though it is incremental as it builds on existing deep learning methods.
The paper tackled the problem of extracting reliable minutiae from low-quality latent fingerprints by using a fully convolutional network to learn features directly from data, achieving a 53% recall and precision rate on the NIST SD27 database and processing each fingerprint in 0.45 seconds on average.
Minutiae play a major role in fingerprint identification. Extracting reliable minutiae is difficult for latent fingerprints which are usually of poor quality. As the limitation of traditional handcrafted features, a fully convolutional network (FCN) is utilized to learn features directly from data to overcome complex background noises. Raw fingerprints are mapped to a correspondingly-sized minutia-score map with a fixed stride. And thus a large number of minutiae will be extracted through a given threshold. Then small regions centering at these minutia points are entered into a convolutional neural network (CNN) to reclassify these minutiae and calculate their orientations. The CNN shares convolutional layers with the fully convolutional network to speed up. 0.45 second is used on average to detect one fingerprint on a GPU. On the NIST SD27 database, we achieve 53\% recall rate and 53\% precise rate that outperform many other algorithms. Our trained model is also visualized to show that we have successfully extracted features preserving ridge information of a latent fingerprint.