Grip-Pattern Recognition in Smart Gun Based on Likelihood-Ratio Classifier and Support Vector Machine


Share/Save/Bookmark

Shang, Xiaoxin and Veldhuis, Raymond N.J. (2008) Grip-Pattern Recognition in Smart Gun Based on Likelihood-Ratio Classifier and Support Vector Machine. In: 3rd International Conference on Image and Signal Processing, ICIP 2008, 1-3 July 2008, Cherbourg-Octeville, France (pp. pp. 289-295).

[img] PDF
Restricted to UT campus only
: Request a copy
406kB
Abstract:In the biometric verification system of a smart gun, the rightful user of a gun is recognized based on grip-pattern recognition. It was found that the verification performance of this system degrades strongly when the data for training and testing have been recorded in different sessions with a time lapse. This is due to the variations between the probe image and the gallery image of a subject. In this work the grip-pattern verification has been implemented based on both classifiers of the likelihood-ratio classifier and the support vector machine. It has been shown that the support vector machine gives much better results than the likelihood-ratio classifier if there are considerable variations between data for training and testing. However, once the variations are reduced by certain techniques and thus the data are better modelled during the training process, the support vector machine tends to lose its superiority.
Item Type:Conference or Workshop Item
Copyright:© 2008 Springer
Faculty:
Electrical Engineering, Mathematics and Computer Science (EEMCS)
Research Group:
Link to this item:http://purl.utwente.nl/publications/62576
Official URL:http://dx.doi.org/10.1007/978-3-540-69905-7_33
Export this item as:BibTeX
EndNote
HTML Citation
Reference Manager

 

Repository Staff Only: item control page

Metis ID: 254945