  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.
