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National Repository of Grey Literature 2 records found  Search took 0.01 seconds. 
Fingerprint Recognition with Graph Neural Networks
 Pospíšil, Ondřej ; Špaňhel, Jakub (referee) ; Hradiš, Michal (advisor)
This thesis deals with the verification of fingerprints based on their graph representation. The proposed method uses a graph neural network and a combinatorial solver to obtain the matching between the minutae points of a pair of fingerprints. The matched minutae points are used to align the fingerprints using an estimated transformation by the RANSAC algorithm. The aligned fingerprints are processed by the SimGNN model. The resulting similarity score is then combined with the metrics obtained from the aligned fingerprints. The experiments summarize the selection of method parameters and the evaluation of fingerprint matching and verification accuracy. The contribution of this work is a new stable method of fingerprint alignment by solving the graph matching problem. The proposed verification method does not achieve high accuracy due to too few minutae attributes and poor discriminating power of the metrics used.
Detailed record
Fingerprint Recognition with Graph Neural Networks
 Pospíšil, Ondřej ; Špaňhel, Jakub (referee) ; Hradiš, Michal (advisor)
This thesis deals with the verification of fingerprints based on their graph representation. The proposed method uses a graph neural network and a combinatorial solver to obtain the matching between the minutae points of a pair of fingerprints. The matched minutae points are used to align the fingerprints using an estimated transformation by the RANSAC algorithm. The aligned fingerprints are processed by the SimGNN model. The resulting similarity score is then combined with the metrics obtained from the aligned fingerprints. The experiments summarize the selection of method parameters and the evaluation of fingerprint matching and verification accuracy. The contribution of this work is a new stable method of fingerprint alignment by solving the graph matching problem. The proposed verification method does not achieve high accuracy due to too few minutae attributes and poor discriminating power of the metrics used.
Detailed record

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