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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{this https URL}
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This content will become publicly available on September 23, 2026
Online Proctoring System with Fingerprint and Eye Recognition Using Siamese Network
The increasing number of online courses and programs available worldwide has elevated the importance of reliable online exam proctoring. The typical proctoring process relies on webcam surveillance. However, this traditional method of proctoring is vulnerable to numerous types of face occlusions used for religious reasons or otherwise. We present a robust biometric authentication framework that combines advanced eye and face recognition, resulting in a much better live proctoring system that is further augmented by including fingerprinting. We have used cutting-edge deep learning techniques, specifically the Siamese network for fingerprint analysis and a ResNet-based eye recognition model tested with and without Gabor filters. Furthermore, our system offers much better performance compared to previously existing models. Notably, our system maintains high accuracy, approximately 98.04% for custom eye recognition, 99.01% for publicly available labeled faces dataset, 82% for niqab dataset and 87.04% for publicly available fingerprint dataset. Moreover, our model demonstrates a 10–20% improvement in face recognition under occlusion. Our solution is highly effective for not only online proctoring but also allows use in other similar situations, such as employee authentication for remote presence verification. Our system supports scenarios involving partial occlusions, such as masks and sunglasses, to full occlusions with veils, without requiring any additional hardware.
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- Award ID(s):
- 2318574
- PAR ID:
- 10649912
- Publisher / Repository:
- World Scientific Publishing Company
- Date Published:
- Journal Name:
- Journal of Circuits, Systems and Computers
- ISSN:
- 0218-1266
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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