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Towards an Open Framework for Mobile Digital Identity Management through Strong Authentication Methods


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Since, today, a wide and variety of applications require reliable verification schemes to confirm the identity of an individual, recognizing humans based on their body characteristics became more and more interesting in emerging technology applications. Biometric cannot be borrowed, stolen, or forgotten, and forging one is practically impossible. Fingerprints are the only basis for individual identification by biometric authentication process. Password based authentication systems are very very less secure than that of the fingerprint authentication where fingerprints and Iris are the only unique for every Individual. With the emerging use of biometric authentication systems in the past years, spoof fingerprint detection has become increasingly important. In this paper, I take a survey on a static software approach that combines all sorts of fingerprint features.
Fingerprint-based authentication systems have developed rapidly in the recent years. However, current fingerprint- based biometric systems are vulnerable to spoofing attacks. Moreover, the single feature-based static approach does not perform equally over different fingerprint sensors and spoofing materials. In this paper, propose a static software approach. 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. 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.

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