Authors - Poornima E. Gundgurti, Shrinivasrao B. Kulkarni Abstract - Latent fingerprints play a crucial role in forensic investigations, driven by both public demand and advancements in biometrics research. Despite substantial efforts in developing algorithms for latent fingerprint matching systems, numerous challenges persist. This study introduces a novel approach to latent fingerprint matching, addressing these limitations through hybrid optimization techniques. Recognizing latent fingerprints as pivotal evidence in law enforcement, our comprehensive method encompasses fingerprint pre-processing, feature extraction, and matching stages. The proposed latent fingerprint matching utilizes a novel approach named as, Randomization Gravity Search Forest algorithm (RGSFA). Acknowledging the shortcomings of traditional techniques, our method enhances convergence speed and performance evaluation by integrating weighted factors. Precision, recall, F-measure, and recognition rate serve as performance metrics. The proposed approach has a high recognition rate of 99.9% and is successful in identifying and matching latent fingerprints, furthering the development of biometric-based personal verification techniques in forensic science and law enforcement. Experimental analyses, using publicly accessible low-quality latent fingerprints from FVC-2004 datasets, demonstrate that the proposed framework outperforms various state-of-the-art approaches.
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