Finger Selfie
Biometric authentication during the COVID-19 and post-pandemic times require a touchless authentication mechanism. While existing studies showcase the use of fingerphoto for touchless authentication, a short video of the finger can provide many good-quality frames. This research presents the first publicly available finger-video dataset, titled Multi-Movement Finger-Video (MMFV) Database. The MMFV dataset has 3792 videos from 336 classes, acquired over two sessions, and spans three different movement types (pitch, yaw, and roll).
- The database can be downloaded from the following link: MMFV: Multi-Movement Finger-Video Database
- To obtain the password for the compressed file, email the duly filled license agreement to databases@iab-rubric.org with the subject line "License agreement for MMFV: Multi-Movement Finger-Video Database"
NOTE: The license agreement has to be signed by someone having the legal authority to sign on behalf of the institute, such as the head of the institution or registrar. If a license agreement is signed by someone else, it will not be processed further. - This database is available only for research and educational purpose and not for any commercial use. If you use the database in any publications or reports, you must refer to the following paper:
- i) A. Malhotra, M. Vatsa, and R. Singh, MMFV: A Multi-Movement Finger-Video Database for Contactless Fingerprint Recognition. IEEE International Workshop on Biometrics and Forensics (IWBF), pp. 1-6, 2023.
- ii) A. Malhotra, A. Sankaran, M. Vatsa, & R. Singh. On matching finger-selfies using deep scattering networks. IEEE Transactions on Biometrics, Behavior, and Identity Science, 2(4), 350-362, 2020.
Finger Print
The appearance of latent fingerprints varies significantly due to the development techniques, leading to large intra-class variation. We propose a Multi-Surface Multi-Technique (MUST) Latent Fingerprint Database. The database consists of more than 16,000 latent fingerprint impressions from 120 unique classes (120 fingers from 12 participants). Including corresponding exemplar fingerprints (livescan and rolled) and extended gallery, the dataset has nearly 21,000 impressions. It has latent fingerprints acquired under 35 different scenarios and an additional four subsets of exemplar prints captured using live scan sensor and inked-rolled prints. With 39 different subsets, the database illustrates intra-class variations in latent fingerprints. The database has the potential usage towards building robust algorithms for latent fingerprint enhancement, segmentation, comparison, and multi-task learning. We also provide annotations for manually marked minutiae, acquisition PPI, and semantic segmentation masks are also provided.
- The database can be downloaded from the following link.
Multi-Surface Multi-Technique (MUST) Latent Fingerprint Database
- To obtain the password for the compressed file, email the duly filled license agreement to databases@iab-rubric.org with the subject line "License agreement for Multi-Surface Multi-Technique (MUST) Latent Fingerprint Database"
NOTE: The license agreement has to be signed by someone having the legal authority to sign on behalf of the institute, such as the head of the institution or registrar. If a license agreement is signed by someone else, it will not be processed further.
This database is available only for research and educational purpose and not for any commercial use. If you use the database in any publications or reports, you must refer to the following paper: - A. Malhotra, M. Vatsa, R. Singh, K. B. Morris, A. Noore, Multi-Surface Multi-Technique (MUST) Latent Fingerprint Database. IEEE Transactions on Information Forensics and Security, 2023.
Latent Fingerprint
Coming Soon.