  Fingerprints are widely recognized as one of the most unique and reliable
characteristics of human identity. Most modern fingerprint authentication
systems rely on contact-based fingerprints, which require the use of
fingerprint scanners or fingerprint sensors for capturing fingerprints during
the authentication process. Various types of fingerprint sensors, such as
optical, capacitive, and ultrasonic sensors, employ distinct techniques to
gather and analyze fingerprint data. This dependency on specific hardware or
sensors creates a barrier or challenge for the broader adoption of fingerprint
based biometric systems. This limitation hinders the widespread adoption of
fingerprint authentication in various applications and scenarios. Border
control, healthcare systems, educational institutions, financial transactions,
and airport security face challenges when fingerprint sensors are not
universally available. To mitigate the dependence on additional hardware, the
use of contactless fingerprints has emerged as an alternative. Developing
precise fingerprint segmentation methods, accurate fingerprint extraction
tools, and reliable fingerprint matchers are crucial for the successful
implementation of a robust contactless fingerprint authentication system. This
paper focuses on the development of a deep learning-based segmentation tool for
contactless fingerprint localization and segmentation. Our system leverages
deep learning techniques to achieve high segmentation accuracy and reliable
extraction of fingerprints from contactless fingerprint images. In our
evaluation, our segmentation method demonstrated an average mean absolute error
(MAE) of 30 pixels, an error in angle prediction (EAP) of 5.92 degrees, and a
labeling accuracy of 97.46%. These results demonstrate the effectiveness of our
novel contactless fingerprint segmentation and extraction tools.
