Latent fingerprint enhancement using Gabor and minutia dictionaries

Latent fingerprints play important roles in law enforcement agencies. Due to its poor quality caused by unclear ridge structure, uneven contrast and overlapping patterns, a latent fingerprint enhancement is necessary for reliable feature extraction. Gabor function is widely used to characterise ridge structure and used in fingerprint enhancement. However, gabor function can not capture the details of minutia that is the end point or bifurcation of ridge. To utilize the prior knowledge of both ridge and minutia, we propose to construct both ridge and minutia dictionaries, and propose a two-step multi-scale patch based sparse representation to enhance the ridge using ridge dictionaries and enhance the minutia with both dictionaries. Experimental results show that two-step SR algorithm outperforms the SR only using gabor dictionary and gabor filter on both minutia extraction accuracy and matching accuracy.