| dc.description.abstract | In recent years, mobile devices such as laptops, smartphones, and tablets have become an integral part of human activities. Accordingly, we need a method to ensure data confidentiality on mobile devices. The fingerprint is one of the unique biometric traits used to authenticate individuals. It is widely used for security issues, including payments, banking, attendance, and securing belongings. However, recognizing the fingerprint image on a mobile device is difficult since the fingerprint reader is only 10 x 10 mm2 in size. The reader captures only some parts of a whole fingerprint. This result contains only a small amount of information, such as ridges, minutiae, and pores.
We proposed a novel methodology for partial fingerprint recognition. The fingerprint feature was represented using the local feature-based AKAZE. These features were selected because they could maintain the fingerprint image′s noise, scale, and orientation. Moreover, we formulated the standard of matching tasks using a sliding window. This sliding window made it possible to compare full images and partial queries comprehensively. As part of this approach, we also replaced the heuristic method of calculating the matching rate with neural networks. A neural network had proven to be able to distinguish between a variety of data without the need for a lot of rules.
As validation, we experimented with our method using an instance of the FVC2002 database. Our method achieves adequate results in terms of biometric evaluation. These values of EER and FRR@FAR 1/5000 are both less than 9%. The highest success rate was recorded in DB1, EER reached 4.95%, and FRR@FAR 1/50000 reached 6.06%. However, these methods only optimize for images of 184x184 pixels. We cannot yet recognize fingerprints when the resolution is reduced. As future research, we must ensure our method work in various image resolutions. Deep learning can help sustain performance across resolutions. As far as computer vision is concerned, deep learning has been proven to be successful in solving complex problems. | en_US |