In order to provide statistical and qualitative backing to latent fingerprint evidence, an algorithm is proposed to discover statistically rare features or patterns in fingerprint images. These features would help establish an objective minimum-quality baseline for latent prints as well as aid in the latent examination process in reaching a matching decision. The proposed algorithm uses minutia triplet-based features in a hierarchical fashion, where minutia points are used along with ridge information toestablish relations between minutiae. Preliminary results show that a set of distinctive features can be found that have sufficient discriminatory power to aid in quality assessment. An example set of 10 statistically rare features is presented, resulting from analysis of a set of 93 images.
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