An Efficient Automatic Attendance System Using Fingerprint Reconstruction Technique

TL;DR

This paper presents a novel fingerprint reconstruction algorithm from minutiae templates to automate attendance systems, revealing vulnerabilities in biometric security by enabling successful attacks.

Contribution

The paper introduces a new fingerprint reconstruction method from minutiae, demonstrating its effectiveness in automating attendance and exposing security flaws.

Findings

Reconstructed fingerprints can successfully attack recognition systems.

Type-I and Type-II attack success rates are high.

Automated attendance system reduces manual effort.

Abstract

Biometric time and attendance system is one of the most successful applications of biometric technology. One of the main advantage of a biometric time and attendance system is it avoids "buddy-punching". Buddy punching was a major loophole which will be exploiting in the traditional time attendance systems. Fingerprint recognition is an established field today, but still identifying individual from a set of enrolled fingerprints is a time taking process. Most fingerprint-based biometric systems store the minutiae template of a user in the database. It has been traditionally assumed that the minutiae template of a user does not reveal any information about the original fingerprint. This belief has now been shown to be false; several algorithms have been proposed that can reconstruct fingerprint images from minutiae templates. In this paper, a novel fingerprint reconstruction algorithm is proposed to reconstruct the phase image, which is then converted into the grayscale image. The proposed reconstruction algorithm reconstructs the phase image from minutiae. The proposed reconstruction algorithm is used to automate the whole process of taking attendance, manually which is a laborious and troublesome work and waste a lot of time, with its managing and maintaining the records for a period of time is also a burdensome task. The proposed reconstruction algorithm has been evaluated with respect to the success rates of type-I attack (match the reconstructed fingerprint against the original fingerprint) and type-II attack (match the reconstructed fingerprint against different impressions of the original fingerprint) using a commercial fingerprint recognition system. Given the reconstructed image from our algorithm, we show that both types of attacks can be effectively launched against a fingerprint recognition system.