Combining global features and local minutiae descriptors in genetic algorithms for fingerprint matching

Abstract

Fingerprint matching is an important and challenging problem in fingerprint recognition. Many approaches have been proposed for fingerprint matching such as minutiae point pattern-based techniques, orientation pattern-based techniques, ridge-based techniques, global and local features combination-based techniques (GLF-BCT). In recent research, GLF-BCT methods achieved good performance even when a large portion of fingerprints in the database are of poor quality. In this paper, we would like to improve the GLF-BCT model using Genetic Algorithm (GA) that we aim to achieve higher efficiency in fingerprint recognition. In detail, the proposed model is a combination of the advantage of local minutiae descriptors (ability of increasing the distinctiveness degree between two different fingerprint images) with the advantage of the global features (identifying the optimal or near optimal global alignment between two fingerprints) to improve the reliability of GA fitness assignment in fingerprint matching. This method is called the Fingerprint Matching based on Combining Global features and Local minutiae Descriptors in Genetic Algorithms (FM-CGLD-GA). The experimental results on the FVC2004 database show the effectiveness and superiority of the proposed method in comparing to other approaches.