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Combining Level-3 Features with Perspiration Pattern for Robust Fingerprint Recognition


Affiliations
1 Vishvakarma Institute of Information Technology, Pune, India
2 Clarkson University, NY, United States
     

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Level-3 fingerprint features from fingerprint images like pores are difficult to capture and detect, and involve high resolution scanners with higher ppi count. However, these features provide finer information about a fingerprint characteristics. Furthermore, fingerprint pores may be useful in determining liveness of fingerprint in order to prevent spoofing of fingerprint devices. In this study fingerprint pores along the ridges are used for fingerprint matching. Wavelet based fingerprint enhancement techniques are implemented to ease detection of the level-3 features. Delaunay triangulation based alignment and matchingof the fingerprints is performed. The pores are checked for the liveness by perspiration activity in the time series captures.The developed matching scheme is tested for the high resolution data (686 ppi) for 114 live and spoof fingerprint classes. ROC is plotted and EER of 2.97% is obtained.


Keywords

Fingerprints, Level-3 Features, Pores, Perspiration Pattern, Liveness, Wavelets.
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  • Combining Level-3 Features with Perspiration Pattern for Robust Fingerprint Recognition

Abstract Views: 367  |  PDF Views: 3

Authors

Aditya Abhyankar
Vishvakarma Institute of Information Technology, Pune, India
Stephanie Schuckers
Clarkson University, NY, United States

Abstract


Level-3 fingerprint features from fingerprint images like pores are difficult to capture and detect, and involve high resolution scanners with higher ppi count. However, these features provide finer information about a fingerprint characteristics. Furthermore, fingerprint pores may be useful in determining liveness of fingerprint in order to prevent spoofing of fingerprint devices. In this study fingerprint pores along the ridges are used for fingerprint matching. Wavelet based fingerprint enhancement techniques are implemented to ease detection of the level-3 features. Delaunay triangulation based alignment and matchingof the fingerprints is performed. The pores are checked for the liveness by perspiration activity in the time series captures.The developed matching scheme is tested for the high resolution data (686 ppi) for 114 live and spoof fingerprint classes. ROC is plotted and EER of 2.97% is obtained.


Keywords


Fingerprints, Level-3 Features, Pores, Perspiration Pattern, Liveness, Wavelets.