Deep Slap Fingerprint Segmentation for Juveniles and Adults
This work addresses a domain-specific problem in biometrics by improving fingerprint segmentation for juvenile subjects, which is incremental as it adapts an existing method to a new dataset.
The paper tackled the problem of segmenting slap fingerprints into individual prints, revealing that existing methods perform poorly on juvenile data, and introduced a new dataset and a Mask-RCNN-based model (CFSEG) that outperforms the baseline NFSEG for both adults and juveniles, with specific gains in matching accuracy.
Many fingerprint recognition systems capture four fingerprints in one image. In such systems, the fingerprint processing pipeline must first segment each four-fingerprint slap into individual fingerprints. Note that most of the current fingerprint segmentation algorithms have been designed and evaluated using only adult fingerprint datasets. In this work, we have developed a human-annotated in-house dataset of 15790 slaps of which 9084 are adult samples and 6706 are samples drawn from children from ages 4 to 12. Subsequently, the dataset is used to evaluate the matching performance of the NFSEG, a slap fingerprint segmentation system developed by NIST, on slaps from adults and juvenile subjects. Our results reveal the lower performance of NFSEG on slaps from juvenile subjects. Finally, we utilized our novel dataset to develop the Mask-RCNN based Clarkson Fingerprint Segmentation (CFSEG). Our matching results using the Verifinger fingerprint matcher indicate that CFSEG outperforms NFSEG for both adults and juvenile slaps. The CFSEG model is publicly available at \url{https://github.com/keivanB/Clarkson_Finger_Segment}