Simultaneous latent fingerprint recognition

Simultaneous latent fingerprints are clusters of friction ridge impressions deposited concurrently in a crime scene. The analysis of these impressions is a complex task to infer individualization, exclusion or categorize as inconclusive. The problem is further compounded when distinctive features in each latent fingerprint in the cluster are of varying quality or none of the fingerprint has the requisite number of features to reliably arrive at a conclusion. Recently, SWGFAST (Scientific Working Group on Friction Ridge Analysis, Study and Technology) proposed a draft standard for simultaneous impression examination. The approach is manual and requires known reference ten-print for comparing with an unknown simultaneous latent fingerprint. This paper proposes a semi-automatic approach to process and analyze simultaneous latent fingerprints. The proposed algorithm demonstrates that comparisons can be made from a database of ten-prints for a more comprehensive search instead of the time consuming manual approach used by latent fingerprint examiners. The algorithm was implemented using several soft computing and classification approaches and the performance was compared using the simultaneous latent fingerprint database. The results show that 2ν-SVM with RBF kernel gave the best results both in terms of time and accuracy.