Conference Paper

Towards integrating level-3 Features with perspiration pattern for robust fingerprint recognition

Electr. & Comput. Eng Dept., Clarkson Univ., Potsdam, NY, USA
DOI: 10.1109/ICIP.2010.5654261 Conference: Image Processing (ICIP), 2010 17th IEEE International Conference on
Source: IEEE Xplore

ABSTRACT Level-3 fingerprint features from fingerprint images like pores are difficult to capture 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 matching of 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.

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    ABSTRACT: Due to the growing use of biometric technologies in our modern society, spoofing attacks are becoming a serious concern. Many solutions have been proposed to detect the use of fake "fingerprints" on an acquisition device. In this paper, we propose to take advantage of intrinsic features of friction ridge skin: pores. The aim of this study is to investigate the potential of using pores to detect spoofing attacks. Results show that the use of pores is a promising approach. Four major observations were made: First, results confirmed that the reproduction of pores on fake "fingerprints" is possible. Second, the distribution of the total number of pores between fake and genuine fingerprints cannot be discriminated. Third, the difference in pore quantities between a query image and a reference image (genuine or fake) can be used as a discriminating factor in a linear discriminant analysis. In our sample, the observed error rates were as follows: 45.5% of false positive (the fake passed the test) and 3.8% of false negative (a genuine print has been rejected). Finally, the performance is improved by using the difference of pore quantity obtained between a distorted query fingerprint and a non-distorted reference fingerprint. By using this approach, the error rates improved to 21.2% of false acceptation rate and 8.3% of false rejection rate.


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