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Abhyankar, Aditya
- Combining Level-3 Features with Perspiration Pattern for Robust Fingerprint Recognition
Authors
1 Vishvakarma Institute of Information Technology, Pune, IN
2 Clarkson University, NY, US
Source
Digital Image Processing, Vol 1, No 5 (2009), Pagination: 165-169Abstract
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.
- Fingerprint Image Quality and Prediction of Matching Performance
Authors
1 Vishvakarma Institute of Information Technology, Pune, IN
2 Public Company in White Plains, NY, US
3 Electrical and Computer Engineering Department at San Diego State University, San Diego, US
4 Clarkson University, NY, US
Source
Digital Image Processing, Vol 1, No 3 (2009), Pagination: 108-112Abstract
Due to their high reliability, fingerprints have been extensively used as a biometric identifier. The performance of an automatic fingerprint authentication system relies heavily on the fingerprint image quality as seen in several studies. In this work, we present a new method to quantify fingerprint image quality which is relevant to matcher performance. The ultimate goal of this research is to determine and overcome the underlying causes of the poor match. Newly developed wavelet features and previously developed spatial features, as inputs to a fuzzy c-means classifier, are used predict matcher performance. Results are obtained for two different matchers, namely NFIS bozorth3 and Verifinger (Neurotechnologija), for two different optical sensors,Crossmatch Verifier 300 and Secugen Hamster III.