A database of fingerprints - BVC-2016-Noisy-fingerprint data, collected by the Biometrics Vision and Computing (BVC) group
We are making the BVC-2016-Noisy-fingerprint data free and publicly available.
A formal request, via email, is required to use the BVC-2016-Noisy-fingerprint data.
BVC-2016-Noisy-fingerprint dataset comprises a total of 1400 fingerprints of 200 subjects consistently acquired at different conditions which include: various levels of rotations, dirty and moist conditions. The rotations correspond to angles 0º, 10º, 20º and 30º constituting four conditions. The dirty and moist forms are the fifth and sixth conditions.
Images of fingerprints were acquired using a Microsoft fingerprint reader and stored in Bitmap (BMP) format. This database was collected in single-session in 2 days in the Department of Electronic Engineering, University of Nigeria. the size of this database is 97.8MB (zipped).
Two impressions per subject were acquired for the normal condition, 0 , while one impression per subject was acquired for other five conditions.
Each user placed his finger on the scanner, twice for normal orientation, 0 , and once for degrees: 10°, 20° and 30°.
Fingerprints are numbered following this nomenclature Fnnn_C.bmp where: F is a prefix for finger
nnn represents the subject number or identifier and runs from 001 to 200.
C stands for the conditions of acquiring a subject’s image and could be: A0 – symbolizing normal condition 1 A01 – symbolizing normal condition 2 A10d or A10D – symbolizing 10 degrees’ rotation A20d or A20D – symbolizing 20 degrees’ rotation A30d or A30D – symbolizing 30 degrees’ rotation D – symbolizing dusty fingerprints M – symbolizing moist fingerprints
Send your request for our fingerprint dataset to:
When the BVCUN-Index dataset is used in any form of research, please the following paper should be cited:
{ Ogechukwu N. Iloanusi and Celestine A. Ezema, A quantitative impact of fingerprint distortion on recognition performance, Information Security Journal: A Global Perspective, Volume 26, Number 6, 2017, Pages 267-275, ISSN 1939-3555, https://doi.org/10.1080/19393555.2017.1383535 Keywords: Fingerprint, security, biometrics, image quality, distortion, performance, regression analysis
}