Finger Vein Feature Level Fusion Framework
This is "The Multimedia Signal Processing and Security Lab", short WaveLab, website. We are a research group at the Artificial Intelligence and Human Interfaces (AIHI) Department of the University of Salzburg led by Andreas Uhl. Our research is focused on Visual Data Processing and associated security questions. Most of our work is currently concentrated on Biometrics, Media Forensics and Media Security, Medical Image and Video Analysis, and application oriented fundamental research in digital humanities, individualised aquaculture and sustainable wood industry.
This is a feature level fusion framework for finger vein recognition implemented in MATLAB. It was tested on MATLAB 2013b and should work with all version of MATLAB newer or equal to 2013b. This software is under the Simplified BSD license.
The framework contains all the feature extraction and fusion methods used for the experiments in the paper
Feature Extraction Methods
The following 6 feature extractors, all outputting binary vein images, are contained:
There are two different types of fusion:
The following fusion schemes were tested and are contained in the framework:
The main directory contains the setup and the main functions for the two different types of fusion, including the matching/score calculation function. The package further contains the following subdirectories:
The whole framework is written in MATLAB. For more detailed usage instructions have a look at the readme.txt file contained in the package. It depends on the following external software packages, which have to be downloaded seperately:
The UTFVP finger vascular pattern dataset has been utilized which need to be obtained from the University of Twente and should be put into the data directory.
The following methods have not been implemented by ourselves but are already included in the framework sources:
For the Maximum Curvature, Repeated Line Tracking, Wide Line Detector and Principal Curvature feature extraction as well as for the finger boundary detection and the finger normalisation an implementation of B.T. Ton was utilised which is publicly availabale through MATLAB Central. The Gabor Filter approach is a custom implementation of the approach by Kumar et al.  done by Emanuela Piciucco in , which is also included in the fusion framework package.
For matching an implementation of B.T. Ton of the method proposed by Miura et al. [2, 3] which can also be downloaded via MATLAB Central was used.
For the EER determination the routine of the Biosecure Tool was utilisied which can be found here.
For adaptive thresholding an implementation by Guanglei Xiong, which freely available at MATLAB Central was used.
For Gaussian filtering an implementation by F. van der Heijden, freely available at MATLAB Central was used.
 . A high quality finger vascular pattern dataset collected using a custom designed capturing device. International Conference on Biometrics, ICB 2013, 2013. URL http://doc.utwente.nl/87790/.
 . Extraction of finger-vein patterns using maximum curvature points in image profiles. IEICE transactions on information and systems, 90(8):1185—1194, 2007
 . Feature extraction of finger-vein patterns based on repeated line tracking and its application to personal identification. Machine Vision and Applications, 15(4):194—203, 2004
 . Finger-vein authentication based on wide line detector and pattern normalization. Pattern Recognition (ICPR), 2010 20th International Conference on:1269—1272, 2010
 . Finger vein extraction using gradient normalization and principal curvature. IS&T/SPIE Electronic Imaging:725111—725111, 2009
 . The Undecimated Wavelet Decomposition and its Reconstruction. IEEE Transactions on Image Processing, 16(2):297—309, 2007. URL http://dx.doi.org/10.1109/TIP.2006.887733
 . Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation. Medical Imaging, IEEE Transactions on, 23(7):903—921, 2004
 . Robust statistical fusion of image labels. Medical Imaging, IEEE Transactions on, 31(2):512—522, 2012
 . Robust statistical label fusion through consensus level, labeler accuracy, and truth estimation (COLLATE). Medical Imaging, IEEE Transactions on, 30(10):1779—1794, 2011
available upon request.