The quality assessment of sets of features extracted from patterns of epidermal ridges on our fingers is a biometric challenge problem with implications on questions concerning security, privacy and identity fraud. In this work, we introduced a new methodology to analyze the quality of high-resolution fingerprint images containing sets of fingerprint pores. Our approach takes into account the spatial interrelationship between the considered features and some basic transformations involving point process and anisotropic analysis. We proposed two new quality index algorithms following spatial and structural classes of analysis. These algorithms have proved to be effective as a performance predictor and as a filter excluding low-quality features in a recognition process. The experiments using error reject curves show that the proposed approaches outperform the state-of-the-art quality assessment algorithm for high-resolution fingerprint recognition, besides defining a new method for reconstructing their friction ridge phases in a very consistent way.

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.

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The segmentation task is an important step in automatic fingerprint classification and recognition. In this context, the term refers to splitting the image into two regions, namely, foreground and background. In this paper, we introduce a novel segmentation approach designed to deal with fingerprint images originated from different sensors. The method considers a multiscale directional operator and a scale-space toggle mapping used to estimate the image background information. We evaluate our approach on images of different databases, and show its improvements when compared against other well-known state-of-the-art segmentation methods discussed in literature.