Citation: Communication Papers of the 2017 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 13, pages 207–212 (2017)
Abstract. This project was developed aiming at the implementation of a multibiometric systemcapable to handle hand palm images acquired using a touchless approach. Thisconsiderable increases the difficult of the image processing task due to the fact thatthe images from the same person may vary significantly depeding on the relative position of the hand regarding the sensor. A modular sofware tool was developed, providing the user a method for each of these steps: initialimage preparation, the feature extraction, processing and fusion, ending withthe classification, thus making the researcher'stask a lot easier and faster. The biometric features used for identification includehand geometry features as well palm vein textures. For the hand geometry data, analgorithm for determining finger tips and hand valleys was proposed and from there was possibleto extract a handful of other features related to the geometry of the hand. The handpalm veins' texture features were extracted from a rectangle generated based on thehand's center of mass. The texture descriptor chosen was the Histogram ofGradients. In possession with all the biometric data, the fusion was done on featurelevel. Support Vector Machine technique was used for the classification. Thedatabase chosen for the development of this project was the CASIA Multi- SpectralPalmprint Image Database V1.0. The images used corresponds to the 940nmspectrum due to allowing the visualization of the hand palm's veins. The achievedresult for the hand geometry was an EER of 4,77\%, for the palm veins an EER of3,11\% and changing the threshold value a FAR of 0,50\% and a FRR of 4,82\% wereachieved. For the fusion of both biometrics systems the final result was an EER of 2,33\% witha FAR of 1,30\% and a FRR of 4,27\%.
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