home  |   research   |  members   |  projects  |  publications   |  conferences of interest   |  downloads   |  contact  |  intern
 
 

Rotation Detection in Finger Vein Biometrics using CNNs

This is "The Multimedia Signal Processing and Security Lab", short WaveLab, website. We are a research group at the Computer Sciences Department of the University of Salzburg headed by Andreas Uhl. The short name "WaveLab" already indicates that wavelets are among our favorite tools - we have 15 years of experience in this area. Our research is focused on Multimedia Security including Watermarking, Image and Video Compression, Medical Image Classification, and Biometrics.

Abstract

Finger vein recognition deals with the identificationof subjects based on their venous pattern within the fingers. Therecognition accuracy of finger vein recognition systems suffersfrom different internal and external factors. One of the majorproblems are misplacements of the finger during acquisition. Inparticular longitudinal finger rotation poses a severe problemfor such recognition systems. The detection and correction ofsuch rotations is a difficult task as typically finger vein scannersacquire only a single image from the vein pattern. Therefore,important information such as the shape of the finger or thedepth of the veins within the finger, which are needed forthe rotation detection, are not available. This work presents aCNN based rotation detector that is capable of estimating therotational difference between vein images of the same fingerwithout providing any additional information. The experimentsexecuted not only show that the method delivers highly accurateresults, but it also generalizes so that the trained CNN can alsobe applied on data sets which have not been included duringthe training of the CNN. Correcting the rotation differencebetween images using the CNN’s rotation prediction leads toEER improvements between 50-260% for a well-established vein-pattern based method (Maximum Curvature) on four public fingervein databases.

Reference

[Prommegger20c   ] Rotation Detection in Finger Vein Biometrics using CNNs Bernhard Prommegger, Georg Wimmer, Andreas Uhl In Proceedings of the 25th International Conference on Pattern Recognition (ICPR), pp. 1-7, 2020, accepted

Data Sets

PLUSVein Finger Rotation Data Set

The PLUSVein Finger Rotation Data Set (PLUSVein-FR) is a partly publically available finger vein data set. It contains finger images captured all around the finger from 63 different subjects, 4 fingers (index and middle finger from both hands) per subject, which sums up to a total of 252 unique fingers. Further information regarding the data set can be found by the following link:

PLUSVein-FR Data Set

PROTECT MultiModal Data Set

The PROTECT multimodal data set (PMMDB) Database consists of simultaneously acquired finger vein and finger surface texture images. The PMMDB includes different biometric modalities, namely iris, face (visual light, NIR, 3D and thermal), periocular, anthropometrics and hand- and finger veins of 69 different subjects. It was acquired in two data acquisition events with one year between the two sessions. Further information regarding the data set can be found by the following link:

PMMDB Download Page (external link)

Finger Vein USM Database

Finger Vein USM (FV-USM) Database is a publically available finger vein data set. It contains finger vein images from 123 volunteers, four fingers each (left and right index and middle finger). The data was captured in two different sessions, capturing six samples per finger in each session. Further information regarding the data set can be found by the following link:

FV-USM Download Page (external link)

PLUSVein-FV3 Finger Vein Data Set

The PLUSVein-FV3 Finger Vein Data Set (PLUSVein-FV3) is a publically available fingr vein data set. It contains palmar and dorsal images of 360 fingers from 60 different subjects (ring, middle and index finger from both hands) captured in one session with five samples per finger using two different variants of the same sensor: One utilizing NIR laser modules for illumination, the other one using NIR LEDs. Further information regarding the data set can be found by the following link:

PLUSVein-FV3 Data Set

SDUMLA-HMT Database

The SDUMLA-HMT database is a multimodal biometric database that contains samples for face, gait, iris, fingerprint and finger veins from 106 individuals. The finger vein subset contains six fingers (ring, middle and index finger from both hands) per subject, captured in one session taking six images of each finger. Further information regarding the data set can be found by the following link:

SDUMLA-HMT Download Page (external link)

University of Twente Finger Vascular Pattern Database

The University of Twente Finger Vascular Pattern (UTFVP) Database is a publically available finger vein data set. It contains six fingers (ring, middle and index finger from both hands) from 60 volunteers acquired in two sessions. Further information regarding the data set can be found by the following link:

UTFVP Download Page (external link)

DOWNLOAD: CNN Models

The download files are available upon request. Please fill out this form to request a download link for the trained CNN models:

Name:
Affiliation:
Email address: