Latent Fingerprint Enhancement Based On Directional Total Variation Model With Lost Minutiae Reconstruction

Latent fingerprint matching assists for law enforcement agencies to identify criminals. Image enhancement plays an important role in automatic latent fingerprint segmentation and matching systems. Even-though sufficient progress done in both rolled and plain fingerprint images enhancement, latent fingerprint enhancement still a challenging problem and existing issue in the current research. This is due to the existence of poor quality images in latent fingerprint with unclear ridge structure and various overlapping patterns together with presence of structured noise. Prior to latent fingerprint segmentation and feature extraction, latent fingerprint image enhancement is necessary step to suppress different noises and improve the clarity of ridge structure. This paper reviews the current techniques used for the latent fingerprint enhancement. Thus, it presents hybrid model which is combination of edge directional total variation model (EDTV) and quality image enhancement with lost minutia reconstruction. NIST SD27 database has been used to test the proposed techniques and RMSE, PSNR to measure the performance. The result of the proposed technique shows enhancement of clarity of input latent fingerprint images and well de-noising of good, bad and ugly images of latent fingerprint. There is a statistically significant difference in the mean length of PSNR and RMSE for different categories of the latent fingerprint images (good, bad and ugly). It's observed that the proposed technique performs well for the good latent fingerprint images compare to bad and ugly images. The result after enhancement present RMSE average 0.018373, 0.022287, and 0.023199 for the three different image categories available in SD27 data set good, bad and ugly images respectively while the PSNR average achieved 82.99068, 81.39749, and 81.07826 respectively. The proposed enhancement technique improved the matching accuracy of latent fingerprint images about 30%.