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2021
Bachelor Thesis
Titel
Towards Unsupervised Fingerprint Image Quality Assessment
Abstract
Fingerprint matching is one of the most popular and reliable biometric techniques used in automatic verification of a person. During the verification process of a fingerprint, the areas and in more detail the information in these areas (minutiae) of the fingerprint are compared with the corresponding information of a second fingerprint, which is called fingerprint matching. However, the quality of the fingerprint images highly affects the recognition process. Generally, the biometric sample quality is defined on its impact on a biometric recognition system, the similarity between the sample and its source, and the quality of its physical features. Poor quality fingerprints often have areas or regions that are not clear or even missing, resulting in arbitrary changes in the structure of these areas. That results in an inaccurate matching caused by comparisons of damaged areas, which leads to inaccurate matching influencing the verification results again. That is the reason why assessing the fingerprint image quality is very important because the matching performance could be significantly affected by poor quality samples. One application area that is very much affected by this problem is forensics. Forensics often deal with partial fingerprints lifted from a surface, known as latent fingerprints. Since latent fingerprints are often of poor quality, the matching performance during the recognition process is often severely impaired. Quality assessment can be used to improve biometric systems that perform automatic recognition tasks like identification or verification. Quality estimation during the enrollment process is used to ensure the best possible quality of the biometric data, thus guaranteeing a good training of the biometric systems as well as a good performance. This is a typical reason why image quality assessment is required to evaluate the quality of the images and improve the recognition process. In this thesis, two new methods are proposed to accurately assess (a) the quality of a single minutia and (b) the quality of a fingerprint. The proposed minutia quality is based on its detection reliability. A minutia is classified by randomly generated subnetworks of a minutiae classifier that determines if a minutia is a true minutia or not. The various classification results are used to determine a robustness score, considered as the detection reliability. The proposed fingerprint quality assessment method applies a stochastic method to the detection reliabilities of the minutiae to determine the quality of the fingerprint. Since this thesis addresses these two problems, both kinds of quality assessments have to be evaluated separately. The proposed minutia quality assessment method (a) is compared with Mindtct. For evaluation purposes of (b), the proposed method is compared with the current state-of-the-art quality assessment method NFIQ2 as well as its predecessor NFIQ. All experiments are evaluated on the FVC2006 database using the Bozorth3 and MCC fingerprint matchers. It can be shown that the proposed method assesses the quality on minutiae-level (a) just as good and better as Mindtct on the most experiments without the need of handcrafted quality labels. Experiments on data acquired from an electrical field sensor, for example, show that the proposed method achieves on average a 0.03 lower FNMR at a FMR of 10−2 using Bozorth3. Furthermore, the quality assessment on fingerprint-level (b) outperforms the state-of-the-art quality assessment methods NFIQ2 and NFIQ. An improvement of the recognition performance on all databases captured by real sensor types could be achieved. Observations of experimental results at a FMR of 10−2 using Bozorth3 show that the proposed method achieves a FNMR that is about 0.003 lower than the FNMR achieved by NFIQ2 on optical sensor data after rejecting the 20% worst fingerprints. Furthermore, a 0.025 lower FNMR can also be achieved on data captured by a thermal sweeping sensor.
ThesisNote
Darmstadt, TU, Bachelor Thesis, 2021
Verlagsort
Darmstadt