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Fingerprint Pre-Alignment based on Deep Learning
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Datum
2019
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Gesellschaft für Informatik e.V.
Zusammenfassung
Robust fingerprint pre-alignment is vital for identification systems and biometric cryptosystems
based on fingerprint minutiae, where computation of a relative alignment by comparison
of the fingerprints is inefficient or intractable, respectively. The pre-alignment is achieved through
an absolute alignment, i. e. an alignment computed for each fingerprint independently, which can be
applied for fingerprint registration to compensate for variations in the placement (translation) and
rotation of the fingerprints prior to their comparison.
In this work, a deep learning approach for absolute pre-alignment of fingerprints is presented. The
proposed algorithm employs a siamese network (with CNNs as subnetworks) which is trained on
synthetically generated fingerprints using horizontal/vertical translation and rotation as three regression
coefficients. Evaluations are conducted on the FVC2000 DB2a and the MCYT fingerprint
database. Compared to other published fingerprint pre-alignment methods, the presented scheme
achieves higher accuracy w. r. t. rotation estimation and overall robustness. In addition, the proposed
pre-alignment is applied as a pre-processing step in a Fuzzy Vault scheme.