Fingerprint enhancement using contextual iterative filtering

The performance of Automatic Fingerprint Identification Systems (AFIS) relies on the quality of the input fingerprints, so the enhancement of noisy images is a critical step. We propose a new fingerprint enhancement algorithm that selectively applies contextual filtering starting from automatically-detected high-quality regions and then iteratively expands toward low-quality ones. The proposed algorithm does not require any prior information like local orientations or frequencies. Experimental results over both real (FVC2004 and FVC2006) and synthetic (generated by the SFinGe software) fingerprints demonstrate the effectiveness of the proposed method.