Criminological Fingerprint Matching on Partial Prints with Hough Transformed Minutiae Descriptors

  • P. Jayasri Archana Devi, M. Jayanthi, S. Selvakumaran, P. S. Satheesh, M. Pavithra Rao, B. Gobinathan

Abstract

 Fingerprint authentication systems play a vital role in identifying an individual. The existing fingerprint authentication systems depend on specific points known as minutiae for recognizing an individual. Designing a reliable automatic fingerprint authentication system is still very challenging, since not all fingerprint information is available. Further, the task of matching the fingerprint becomes more challenging due to factors like partial fingerprints, print smudging, latent print overlaps etc that are lifted out of a crime scene. Various local feature detectors such as Difference-of Gaussian, Hessian, Hessian Laplace, Harris Laplace, Multi scale Harris, and Multi scale Hessian have been extensively used for feature detection. However, these detectors have not been employed for detecting fingerprint image features. In this project a versatile local feature fingerprint matching algorithm Hough transform is proposed. This algorithm considers local characteristic features of the fingerprint image, thus eliminating the issues caused in existing fingerprint feature based matching techniques. The Hough transform is a feature extraction technique used in image analysis, computer vision, and processing. The purpose of the technique is to find imperfect instances of objects within a certain class of shapes by a collective maximizing procedure. This procedure is carried out in a parameter space, from which object candidates are obtained as local maxima in a so-called accumulator space that is explicitly constructed by the algorithm for computing the Hough transform. In our project we combine the powerful features of the Hough transform along with other feature detection methods to provide a real-time system that is robust to be used for criminal applications. The project also includes features like collective fingerprint merging, secure image to hash algorithms and hash collision detection and recovery to further improve upon the security of the overall procedure.

Published
2020-03-20
How to Cite
P. Jayasri Archana Devi, M. Jayanthi, S. Selvakumaran, P. S. Satheesh, M. Pavithra Rao, B. Gobinathan. (2020). Criminological Fingerprint Matching on Partial Prints with Hough Transformed Minutiae Descriptors. International Journal of Control and Automation, 13(02), 1798-1803. Retrieved from http://sersc.org/journals/index.php/IJCA/article/view/38105
Section
Articles