Fingerprint enhancement using Hierarchical Markov Random Fields

We propose a novel approach to enhance the fingerprint image and extract features such as directional fields, minutiae and singular points reliably using a Hierarchical Markov Random Field Model. Unlike traditional fingerprint enhancement techniques, we use previously learned prior patterns from a set of clean fingerprints to restore a noisy one. We are able to recover the ridge and valley structure from degraded and noisy fingerprint images by formulating it as a hierarchical interconnected MRF that processes the information at multiple resolutions. The top layer incorporates the compatibility between an observed degraded fingerprint patch and prior training patterns in addition to ridge continuity across neighboring patches. A second layer accounts for spatial smoothness of the orientation field and its discontinuity at the singularities. Further layers could be used for incorporating higher level priors such as the class of the fingerprint. The strength of the proposed approach lies in its flexibility to model possible variations in fingerprint images as patches and from its ability to incorporate contextual information at various resolutions. Experimental results (both quantitative and qualitative) clearly demonstrate the effectiveness of this approach.