2 research outputs found
Fingerprints: Fixed Length Representation via Deep Networks and Domain Knowledge
We learn a discriminative fixed length feature representation of fingerprints
which stands in contrast to commonly used unordered, variable length sets of
minutiae points. To arrive at this fixed length representation, we embed
fingerprint domain knowledge into a multitask deep convolutional neural network
architecture. Empirical results, on two public-domain fingerprint databases
(NIST SD4 and FVC 2004 DB1) show that compared to minutiae representations,
extracted by two state-of-the-art commercial matchers (Verifinger v6.3 and
Innovatrics v2.0.3), our fixed-length representations provide (i) higher search
accuracy: Rank-1 accuracy of 97.9% vs. 97.3% on NIST SD4 against a gallery size
of 2000 and (ii) significantly faster, large scale search: 682,594 matches per
second vs. 22 matches per second for commercial matchers on an i5 3.3 GHz
processor with 8 GB of RAM
Learning a Fixed-Length Fingerprint Representation
We present DeepPrint, a deep network, which learns to extract fixed-length
fingerprint representations of only 200 bytes. DeepPrint incorporates
fingerprint domain knowledge, including alignment and minutiae detection, into
the deep network architecture to maximize the discriminative power of its
representation. The compact, DeepPrint representation has several advantages
over the prevailing variable length minutiae representation which (i) requires
computationally expensive graph matching techniques, (ii) is difficult to
secure using strong encryption schemes (e.g. homomorphic encryption), and (iii)
has low discriminative power in poor quality fingerprints where minutiae
extraction is unreliable. We benchmark DeepPrint against two top performing
COTS SDKs (Verifinger and Innovatrics) from the NIST and FVC evaluations.
Coupled with a re-ranking scheme, the DeepPrint rank-1 search accuracy on the
NIST SD4 dataset against a gallery of 1.1 million fingerprints is comparable to
the top COTS matcher, but it is significantly faster (DeepPrint: 98.80% in 0.3
seconds vs. COTS A: 98.85% in 27 seconds). To the best of our knowledge, the
DeepPrint representation is the most compact and discriminative fixed-length
fingerprint representation reported in the academic literature.Comment: to appear in IEEE Transactions on Pattern Analysis and Machine
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