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Biometric Encryption using Enhanced Finger Print Image and Elliptic Curve


G. Mary Amirtha Sagayee, S Arumugam, G.S.Anandha Mala


Vol. 13  No. 7  pp. 106-112


The greatest strength of biometrics is that it does not change over time. But at the same time while using it directly for enhancing the security in network system, if that data has been compromised, its compromised forever[1]. Therefore, cancellable biometrics will increase the privacy which means that the true biometrics are never stored or revealed to the authentication server. Biometrics, cryptography and data hiding will provide good perspectives for information security. Most of the researchers confirmed that the finger print is widely used than the iris or face and more over it is the primary choice for most privacy concerned applications. Also many mathematicians proved that Elliptic Curve is the best solution for Cryptography[10]. For finger prints applications, choosing proper sensor is at risk. The proposed work deals about, how the image quality can be improved by introducing image fusion technique at sensor levels. The results of the images after introducing the decision rule based image fusion technique are evaluated and analyzed with its entropy levels and root mean square error. Then the resultant enhanced image is used for extracting the key for ECC applications.


Finger Print Image, Wavelet Neural Network, Image Fusion, Entropy, RMSE, Cryptography, ECC, Prime Field.