A Multistep Fusion Matcher Approach for Large Scale Latent Fingerprint/Palmprint Recognition

نویسندگان

چکیده

Latent fingerprints are ubiquitously used as forensic evidence by law enforcement agencies in solving crimes. However, due to deformations and artifacts within latent fingerprint images, performance of the automated recognition systems far from desired levels. A basic matcher specifically designed for clean using a minutiae-based matching algorithm can have high speed accuracy sensor-to-sensor task, but low prints, scale, rotation quality differences between sensor images. In this study, we propose unique multistep fusion (FM) on top base that would utilize rotation, attributes minutiae with speed, memory, trade options process. FM match characteristics analyzed private dataset consisting 5560 1M slap/rolled addition, 292 domain expert selected latents compare nationwide proposed method. FM's multiresolution (MRF) option achieved competitive rates when searching against 1 million background projecting predictions 69 background. On NIST SD302 public dataset, FM6 (FM prioritizing latent-to-sensor search) MRF correctly recognizes 911 rank-1, while COTS system referenced documentation only 790 gallery composed 5950 100K rolled database. rank-1 count 10K is 1415, whereas NIST?s 880 same dataset. construct 722 pairs evaluate latent-to-latent performance. FM8 has 46.1% identification rate search Moreover, 1457 palmprint versus 2296 background, palm dividing images into 512x512 pixel segments produces 85.45% FM6.

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ژورنال

عنوان ژورنال: Turkish Journal of Electrical Engineering and Computer Sciences

سال: 2023

ISSN: ['1300-0632', '1303-6203']

DOI: https://doi.org/10.55730/1300-0632.3992