Automatic latent fingerprint identification system using scale and rotation invariant minutiae features

In this paper, we propose a new clustered minutiae-based scale and rotation invariant fingerprint matching method. The major challenge faced in the existing latent fingerprint identification system is the lack of minutiae features in the fingerprint regions and hence there is a requirement to utilize the existing minutiae arrangements in the regions to identify the query fingerprint. We have clustered minutiae around a reference minutia and generated minutiae invariants to identify the fingerprint. In this paper, we propose two algorithms based on the minutiae neighborhood. To solve the geometrical constraints between the pairs of nearest points around a minutia, we propose the latent minutiae similarity (LMS) algorithm. Based on geometrical arrangements on the set of latent minutiae patterns around a minutia, we propose a clustered latent minutiae pattern (CLMP) algorithm. We test our algorithms on the FVC2004 and NIST SD27 criminal fingerprint databases. Proposed LMS, CLMP algorithms produced the highest 97.5% and 100% of Rank-1 identification accuracy respectively on plain FVC2004 dataset. Whereas, for NIST SD27 latent fingerprint database the proposed LMS, CLMP algorithms produced the highest Rank-1 identification accuracy of 88.8% and 93.80% respectively. Experimental results show significant improvement in the Rank-1 matching accuracy under random fingerprint scale and rotation condition compared to the state-of-the-art algorithms. © 2020, Bharati Vidyapeeth's Institute of Computer Applications and Management.