Fingerprint Feature Extraction and Classification Based on Macroscopic Curvature

In an automatic fingerprint identification system （AFIS）, feature extraction is a critical step For effectively using the curve information to describe fingerprint, a novel algorithm is proposed; it embraces information of few fingerprint ridges nearby to extract a new characteristic which can describe the curvature feature of fingerprint Furthermore a new classification method based on macroscopic characteristics is proposed. Experimental results demonstrate that the algorithm is feasible, and the characteristics extracted by it can clearly show the inner macroscopic curve properties of the fingerprint image The result also shows that this kind of characteristic is robust to noise and pollution, e g those from additive Gaussian noise, and can be applied in fingerprint verification as a supplement to the customary minutiae The searching space in matching process is greatly reduced with the aid of the new classification method