A Novel Approach for Partial Fingerprint Identification to Mitigate MasterPrint Generation

TL;DR

This paper introduces a new method for partial fingerprint identification that reduces the occurrence of MasterPrints, thereby enhancing security and accuracy in biometric systems, especially with small sensors and forensic applications.

Contribution

It proposes a novel approach to mitigate MasterPrint generation in partial fingerprint recognition, improving system reliability and security.

Findings

Significant reduction in MasterPrints using the proposed method

Improved True Match Rate (TMR) and Equal Error Rate (EER)

Enhanced robustness of partial fingerprint identification

Abstract

Partial fingerprint recognition is a method to recognize an individual when the sensor size has a small form factor in accepting a full fingerprint. It is also used in forensic research to identify the partial fingerprints collected from the crime scenes. But the distinguishing features in the partial fingerprint are relatively low due to small fingerprint captured by the sensor. Hence, the uniqueness of a partial fingerprint cannot be guaranteed, leading to a possibility that a single partial fingerprint may identify multiple subjects. A MasterPrint is a partial fingerprint that identifies at least 4% different individuals from the enrolled template database. A fingerprint identification system with such a flaw can play a significant role in convicting an innocent in a criminal case. We propose a partial fingerprint identification approach that aims to mitigate MasterPrint generation. The proposed method, when applied to partial fingerprint dataset cropped from standard FVC 2002 DB1(A) dataset showed significant improvement in reducing the count of MasterPrints. The experimental result demonstrates improved results on other parameters, such as True match Rate (TMR) and Equal Error Rate (EER), generally used to evaluate the performance of a fingerprint biometric system.