Journal ArticleDOI
RidgeBase: A Cross-Sensor Multi-Finger Contactless Fingerprint Dataset
Bhavin Jawade,Deen Dayal Mohan,Srirangaraj Setlur,Nalini K. Ratha,Venugopal Govindaraju +4 more
- pp 1-9
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TLDR
RidgeBase as discussed by the authors is a large-scale real-world contactless fingerprint matching dataset that consists of more than 15,000 contactless and contact-based fingerprint image pairs acquired from 88 individuals under different background and lighting conditions using two smartphone cameras and one flatbed contact sensor.Abstract:
Contactless fingerprint matching using smartphone cameras can alleviate major challenges of traditional fingerprint systems including hygienic acquisition, portability and presentation attacks. However, development of practical and robust contactless fingerprint matching techniques is constrained by the limited availablity of large scale real-world datasets. To motivate further advances in contactless fingerprint matching across sensors, we introduce the RidgeBase benchmark dataset. RidgeBase consists of more than 15,000 contactless and contact-based fingerprint image pairs acquired from 88 individuals under different background and lighting conditions using two smartphone cameras and one flatbed contact sensor. Unlike existing datasets, RidgeBase is designed to promote research under different matching scenarios that include Single Finger Matching and Multi-Finger Matching for both contactless-to-contactless (CL2CL) and contact-to-contactless (C2CL) verification and identification. Furthermore, due to the high intra-sample variance in contactless fingerprints belonging to the same finger, we propose a set-based matching protocol inspired by the advances in facial recognition datasets. This protocol is specifically designed for pragmatic contactless fingerprint matching that can account for variances in focus, polarity and finger-angles. We report qualitative and quantitative baseline results for different protocols using a COTS fingerprint matcher (Verifinger) and a Deep CNN based approach on the RidgeBase dataset. The dataset can be downloaded here: https://www.buffalo.edu/cubs/research/datasets/ridgebase-benchmark-dataset.htmlread more
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Proceedings ArticleDOI
MMFV: A Multi-Movement Finger-Video Database for Contactless Fingerprint Recognition
TL;DR: In this paper , the authors presented the first publicly available finger-video dataset, called Multi-Movement Finger-Video (MMFV) Database, which consists of 3792 videos from 336 classes, acquired over two sessions, and spans three different movement types (pitch, yaw, and roll).
CoNAN: Conditional Neural Aggregation Network For Unconstrained Face Feature Fusion
TL;DR: In this article , a feature distribution conditioning approach called CoNAN is proposed for template aggregation, which aims to learn a context vector conditioned over the distribution information of the incoming feature set, which is utilized to weigh the features based on their estimated informativeness.
References
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Journal ArticleDOI
Fingerprint image enhancement: algorithm and performance evaluation
Lin Hong,Yifei Wan,Anil K. Jain +2 more
TL;DR: A fast fingerprint enhancement algorithm is presented, which can adaptively improve the clarity of ridge and valley structures of input fingerprint images based on the estimated local ridge orientation and frequency.
Proceedings ArticleDOI
IARPA Janus Benchmark-B Face Dataset
Cameron Whitelam,Emma Taborsky,Austin Blanton,Brianna Maze,Jocelyn Adams,Tim Miller,Nathan D. Kalka,Anil K. Jain,James A. Duncan,Kristen Allen,Jordan Cheney,Patrick J. Grother +11 more
TL;DR: The IARPA Janus Benchmark-B (NIST IJB-B) dataset is introduced, a superset of IJB -A that represents operational use cases including access point identification, forensic quality media searches, surveillance video searches, and clustering.
Book ChapterDOI
Biometric Sensor Interoperability: A Case Study In Fingerprints
Arun Ross,Anil K. Jain +1 more
TL;DR: This paper discusses the problem of biometric sensor interoperability in biometric systems and presents a case study involving two different fingerprint sensors.
Posted Content
AdaCos: Adaptively Scaling Cosine Logits for Effectively Learning Deep Face Representations
TL;DR: A novel cosine-based softmax loss is proposed, AdaCos, which is hyperparameter-free and leverages an adaptive scale parameter to automatically strengthen the training supervisions during the training process and outperforms state-of-the-art softmax losses on all the three datasets.
Journal ArticleDOI
Matching Contactless and Contact-Based Conventional Fingerprint Images for Biometrics Identification
Chenhao Lin,Ajay Kumar +1 more
TL;DR: Robust thin-plate spline (RTPS) is developed to more accurately model elastic fingerprint deformations using splines and RTPS-based generalized fingerprint deformation correction model (DCM) is proposed, which results in accurate alignment of key minutiae features observed on the contactless and contact-based fingerprints.