Ilja Tscherkassow defended his Master's thesis on 'Quality Metrics for multi-layer Fingerprint Samples'

To reduce the attack vulnerability of automated biometric recognition systems based on the recognition of the fingerprint characteristic features, it required the design and implementation of a new fingerprint-scanning technology. The new technology could be implemented over time based on optical coherence tomography.

An essential advantage of this new technology is the possibility of acquiring three- dimensional images of the fingerprint characteristic feature. This produces, built on the coherence tomography fingerprint reader as a result of detection, multi-layered or three-dimensional fingerprint samples.

With the introduction of a new class of fingerprint scanners and therefore also a new class of fingerprint samples, new algorithms must be created for processing the new class of fingerprint samples.

The aim of this preparation is the formation and realisation of a new quality algorithm for the estimation of the new class of fingerprint samples. The new quality algorithm was realised based on the Tensor Voting Framework. Its performance was evaluated on a data set consisting out of multi-layer fingerprint samples. The result of the evaluation did show that, the performance of the new quaulity algorithm is outperformed by most of the state of the art quality algorithms.

Ilja Tscherkassow defended his Master's thesis on 'Quality Metrics for multi-layer Fingerprint Samples'