Compressed Fingerprint Matching and Camera Identification via Random Projections

Compressed Fingerprint Matching and Camera Identification via Random Projections

Abstract—Compressed Fingerprint Matching and Camera Identification via Random Projections. Sensor imperfections in the form of photoresponse nonuniformity (PRNU) patterns are a well-established fingerprinting technique to link pictures to the camera sensors that acquired them. The noise-like characteristics of the < Final Year Projects 2016 > PRNU pattern make it a difficult object to compress, thus hindering many interesting applications that would require storage of a large number of fingerprints or transmission over a bandlimited channel for real-time camera matching. We propose to use real-valued or binary random projections to effectively compress the fingerprints at a small cost in terms of matching accuracy. The performance of randomly projected fingerprints is analyzed from a theoretical standpoint and experimentally verified on databases of real photographs.