Fingerprint Liveness Detection From Single Image Using Low-Level Features and Shape Analysis Matlab

Fingerprint Liveness Detection From Single Image Using Low-Level Features and Shape Analysis | Matlab Image Processing Final Year IEEE project with source code.
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🚀IEEE Base Paper Title: Fingerprint Liveness Detection From Single Image Using Low-Level Features and Shape Analysis.
💡Implementation Code: MATLAB.
🛠️Tool details: MATLABR2013A.
💰Cost (In Indian Rupees): Rs.3000/

📘IEEE Base paper Abstract:
Fingerprint-based authentication systems have developed rapidly in the recent years. However, current fingerprint-based biometric systems are vulnerable to spoofing attacks. Moreover, single feature-based static approach does not perform equally over different fingerprint sensors and spoofing materials. In this paper, we propose a static software approach. We propose to combine low-level gradient features from speeded-up robust features, pyramid extension of the histograms of oriented gradient and texture features from Gabor wavelet using dynamic score level integration. We extract these features from a single fingerprint image to overcome the issues faced in dynamic software approaches, which require user cooperation and longer computational time. A experimental analysis done on LivDet 2011 data produced an average equal error rate (EER) of 3.95% over four databases. The result outperforms the existing best average EER of 9.625%. We also performed experiments with LivDet 2013 database and achieved an average classification error rate of 2.27% in comparison with 12.87% obtained by the LivDet 2013 competition winner.

📚REFERENCE:
Rohit Kumar Dubey, Jonathan Goh, and Vrizlynn L. L. Thing, “Fingerprint Liveness Detection From Single Image Using Low-Level Features and Shape Analysis”, IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 11, NO. 7, JULY 2016.

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