Singularity Preserving Fingerprint Image Adaptive Filtering

Accurate and reliable detection of minutiae from the fingerprint images is an important factor in the performance of automatic fingerprint identification systems (AFIS). Fingerprint image quality evaluation and appropriate enhancement technique are critical steps for accuracy of minutiae detection algorithm. Most fingerprint enhancement algorithms rely heavily on local orientation of ridge flows. However, significant orientation changes occur around the delta and core points in the fingerprint images, and this poses a challenge to the enhancement of ridge flows in those high-curvature regions. Instead of identifying the singular points, we calculate an orientation coherence map and determine minimum coherence regions as high-curvature areas. Gaussian filter window sizes are adaptively chosen to smooth the local orientation map. Because the smoothing operation is applied to local ridge shape structures, it efficiently joins broken ridges without destroying essential singularities and enforces continuity of directional fields even in creases. To the best the authors´ knowledge, the coherence has not previously been used to estimate ridge curvature, and curvature has not been used to select filter scale in this field. These two strategies are the primary contributions of this paper. Experimental results demonstrate the effectiveness of the proposed method.