Fingerprint enhancement by shape adaptation of scale-space operators with automatic scale selection

This work presents two mechanisms for processing fingerprint images; shape- adapted smoothing based on second moment descriptors and automatic scale se lection based on normalized derivatives. The shape adaptation procedure adapts the smoothing operation to the local ridge structures, which allows interrupted ridges to be joined without dest roying essential singularities such as branching points and enforces contin uity of their directional fields. The Scale selection procedure estimates l ocal ridge width and adapts the amount of smoothing to the local amount of noise. In addition, a ridgeness measure is defined, which reflects how well the local image structure agrees with a qualitative ridge model, and is us ed for spreading the results of shape adaptation into noisy areas. The combined approach makes it possible to resolve fine scale structures in clear areas while reducing the risk of enhancing noise in blurred or fragm ented areas. The result is a reliable and adaptively detailed estimate of t he ridge orientation field and ridge width, as well as a Smoothed grey-leve l version of the input image. We propose that these general techniques should be of interest to developer s of automatic fingerprint identification systems as well as in other appli cations of processing related types of imagery.