Advances in Fingerprint Biometric Recognition for Modern Attendance Management: A State-of-the-Art Review

Abstract

The manual attendance system has been in use for decades; however, shortcomings such as impersonation, ineffective manual record-keeping, and unreliable token-centric methodologies limit its application for this digital age.  Biometric-based fingerprint technique is one of the recently evolving approaches developed to mitigate the shortcomings of manual attendance methods. It is on these premises that this current study examines the principal methodologies employed in fingerprint recognition, focusing on attendance management. Efforts were made to review the recent progress in preprocessing techniques such as normalization, segmentation, orientation estimation, ridge frequency analysis, and Gabor filtering. Also, feature extraction techniques like minutiae-based, ridge-based, and hybrid techniques were extensively analyzed. Furthermore, matching algorithms, performance metrics, and error quantification for evaluating the performance of biometric-based fingerprint systems were examined. The strengths, weaknesses, and associated bottlenecks with the biometric-based fingerprint technique, particularly the management of low-quality prints, scalability assurance, and robustness enhancement, were looked into. The outcome of this review opens up gaps for new research directions in the development of more reliable and efficient fingerprint-based attendance mechanisms.