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Fingerprint authentication using geometric features

Biometric based authentication, particularly for fingerprint authentication systems play a vital role in identifying an individual. The existing fingerprint authentication systems depend on specific points known as minutiae for recognizing an individual. Designing a reliable automatic fingerprint authentication system is still very challenging, since not all fingerprint information is available. Further, the information obtained is not always accurate due to cuts, scars, sweat, distortion and various skin conditions. Moreover, the existing fingerprint authentication systems do not utilize other significant minutiae information, which can improve the accuracy. Various local feature detectors such as Difference-of-Gaussian, Hessian, Hessian Laplace, Harris Laplace, Multiscale Harris, and Multiscale Hessian have been extensively used for feature detection. However, these detectors have not been employed for detecting fingerprint image features. In this article, a versatile local feature fingerprint matching scheme is proposed. The local features are obtained by exploiting these local geometric detectors and SIFT descriptor. This scheme considers local characteristic features of the fingerprint image, thus eliminating the issues caused in existing fingerprint feature based matching techniques. Computer simulations of the proposed algorithm on specific databases show significant improvements when compared to existing fingerprint matchers, such as minutiae matcher, hierarchical matcher and graph based matcher. Computer simulations conducted on the Neurotechnology database demonstrates a very low Equal Error Rate (EER) of 0.8%. The proposed system a) improves the accuracy of the fingerprint authentication system, b) works when the minutiae information is sparse, and c) produces satisfactory matching accuracy in the case when minutiae information is unavailable. The proposed system can also be employed for partial fingerprint authentication.