Improving pore extraction in high resolution fingerprint images using spatial analysis

Pore extraction appears to play an important role in high resolution partial fingerprint recognition and in applications involving large population or high security levels. In this paper, we introduce a novel pore extraction approach which takes into account a new relation concerning their spatial and photometric dependence. This relation is given locally by analyzing distinct pores according to their distance and contrast. We evaluate our approach on high resolution pore extraction database and in an application involving partial fingerprint alignment. The proposed segmentation has proved to be quite general, simple and can accurately extract fingerprint pores in real images with different ridge and valley widths. It is also very robust to noise and according to the considered experiments outperforms well-known state-of-art methods.