ABSTRACTED RADON PROFILES FOR FINGERPRINT RECOGNITION

Conventional minutiae-based fingerprint recognition approaches consider only local characteristics and their accuracy dramatically decreases as the number of available minutiae decreases. We propose new features based on Abstracted Radon Profile (ARP). Proposed method uses global properties of an image and it does not necessitate any heavy preprocessing as in classical methods. By using independent gradual patching via proposed multilayer architecture, local characteristics of an image are also preserved. ARP features have an advantage of being robust to zero mean additive noise. For sparse signal representation, dictionary is constructed from the ARP features of the training samples. Recognition is done by l(1)-minimization with quadratic constraints, so this framework can handle dense noise by exploiting the fact that these errors are often sparse. Experimental results in assessing recognition performance demonstrate the proposed approach outperforms the conventional approaches in correlation and distance based comparisons. Computational time comparison result shows the proposed feature is more efficient than brute-force method of image alignment and promising for handling other pattern recognition problems as well.