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

Abstract

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