Pore Detection in Fingerprints Based on Image Subtraction and Anisotropic Diffusion Filtering

We describe a methodology for extracting and identifying pores in high resolution fingerprint digital images. The key strategy is based on an image subtraction between two fingerprint images with different smoothness levels. The anisotropic diffusion filter is employed to obtain such smoothed fingerprints, in which the pores are preserved in the first fingerprint, while the second one only presents ridges and valleys, but pores are blurred. The subtraction procedure results in a difference image, in which the pores are characterized by the lower magnitudes. After that, we perform a histogram equalization for enhancing pores and a global thresholding to obtain the pores as binary regions. Finally, such binary image is post-processed for removing false pore detections. Experiments were conducted using the PolyU HRF fingerprint image set and the results showed that the proposed methodology outperformed other filtered-based pore extraction methods considering the true and false pore detection rates.