Latent Fingerprint Enhancement Based on Directional Total Variation Model with Lost Minutia Reconstruction

Image enhancement plays an important role in biometric systems, this paper presented automatic latent fingerprint segmentation and matching. While considerable progress has made in both rolled and plain fingerprint image enhancement, latent fingerprint enhancement is a challenging problem due to the poor image quality of latent fingerprint with unclear ridge structures and various overlapping patterns, along with the presence of structured noise. Prior to latent fingerprint segmentation and feature extraction, latent fingerprint image enhancement is important to suppress various types of noise and to clarify the ridge structure. This paper reviews the current techniques used for latent fingerprint enhancement and presents a hybrid model which combines the edge directional total variation model (EDTV) and quality image enhancement with lost minutia reconstruction. The NIST SD27 database is used to test the performance of the proposed techniques with RMSE and PSNR. The proposed technique is effectively clarify input latent fingerprint images and eliminate noise in good, bad and ugly latent fingerprint images. A statistically significant difference, which focused on the mean lengths of PSNR and RMSE for different categories of latent fingerprint, images (good, bad and ugly). The proposed technique performs well for the good latent fingerprint images compare to bad and ugly images. Enhancement respectively presents RMSE averages of 0.018373, 0.022287, and 0.023199 for the good, bad and ugly image SD27 image set, as opposed to 82.99068, 81.39749, and 81.07826 for PSNR. The proposed enhancement technique improved the matching accuracy of latent fingerprint images by about 30%.