The nature of statistical learning theory
The nature of statistical learning theory
A Multichannel Approach to Fingerprint Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fingerprint Matching Using Transformation Parameter Clustering
IEEE Computational Science & Engineering
Fingerprint Indexing Based on Novel Features of Minutiae Triplets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Handbook of Fingerprint Recognition
Handbook of Fingerprint Recognition
Fingerprint Identification Using Delaunay Triangulation
ICIIS '99 Proceedings of the 1999 International Conference on Information Intelligence and Systems
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Fingerprint Indexing Based On Symmetrical Measurement
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Fingerprint Indexing Using Ridge Invariants
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Simultaneous latent fingerprint recognition: a preliminary study
BTAS'09 Proceedings of the 3rd IEEE international conference on Biometrics: Theory, applications and systems
An improved fingerprint indexing algorithm based on the triplet approach
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
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Abstract: 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 2@n-SVM with RBF kernel gave the best results both in terms of time and accuracy.