Abstract
“The friction ridge pattern is a 3D structure which, in its natural state, is not deformed by contact with a surface”. Building upon this rather trivial observation, the present work constitutes a first solid step towards a paradigm shift in fingerprint recognition from its very foundations. We explore and evaluate the feasibility to move from current technology operating on 2D images of elastically deformed impressions of the ridge pattern, to a new generation of systems based on full-3D models of the natural non-deformed ridge pattern itself. There are already a small number of previous studies that have already started scratching the surface of 3D fingerprint recognition and that should not go overlooked. However, the vast majority of these few successful approaches published so far, are based on the reconstruction of fingerprints from multiple 2D images acquired with different lighting conditions (photometric stereo 3D reconstruction) or acquired from different angles (stereo vision 3D reconstruction). Such reconstruction methods lead in general to 2D fingerprints wrapped over the overall volume of the finger. These volumetric fingerprints have shown some promising performance, but still miss the real depth information of the ridge pattern, which, in the best case scenario, is coarsely estimated during the error-prone reconstruction process. In the present work we take one step further, directly acquiring for the first time in a consistent and repeatable manner, full-3D fingerprint models stored as point-clouds, where each point is defined by its $[x,y,z]$ coordinates. This way, the 3D data is directly measured by the sensor, with no post-processing reconstruction stage required. The complete recognition system developed represents as well an alternative to traditional technology based on minutiae detection. It shows that image-based processing algorithms and descriptors can be successfully applied to the new full-3D data, reaching very competitive results and confirming the high distinctiveness of the models.
Highlights
This quote by the famous mathematician and philosopher Gottfried W
In the biometric field in particular, such principle bears a direct relation with the concept of data quality as reflected by the fidelity definition given in the ISO/IEC 29794-1 standard [3]: if the acquired biometric sample does not VOLUME 8, 2020
EVALUATION: EXPERIMENTAL PROTOCOL AND RESULTS The new full-3D fingerprint recognition system presented from Sect
Summary
From the origin of automated fingerprint recognition technology in the early 1960s [6], there has been a huge economic and scientific investment in the development of live-scan 2D touch-based fingerprint sensors. While at some point of the recognition process there is, a volumetric representation of the fingertip, the vast majority of these works are based on acquiring multiple 2D pixel-based images of the finger, and generating a 3D reconstruction from them Very few of these works consider the direct acquisition of a full3D fingerprint model consisting of vertices defined by their [x, y, z] spatial coordinates. This formula relying on ‘‘reconstructed-3D data from multiple 2D samples’’ has had some success, it only partially addresses the issue of the information loss derived from the initial acquisition of 2D images to generate the final volumetric model Another challenge created by this type of approaches is the reconstruction process itself, which is usually computationally expensive and adds an extra errorprone stage to the recognition chain.
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