Fingerprint Recognition Based on Level Three Features

Nowadays one of the most popular topics in fingerprint recognition academic research is the high-resolution fingerprint identification way. This technique has been prominently attractive to the worldwide scientific community thanks to the possibility of using level 3 features like pores which cannot be detected in lower resolution images. In this context, this chapter proposes two contributions: First, a pore detection method for high-resolution fingerprint image based on the morphological operation (skeletonization) and the labeled connect component method. Second, a new method to match pores without an alignment. Our matching approach is based on the contextual characteristics of the pore. It consists of positions and orientations of pore neighbors, which are defined as polar coordinates given in the polar system centered on the considered pore. The proposed algorithms are tested on a high-resolution fingerprint database. Experimental results show that our methods outperform the existing algorithms.

[1]  Jonathan D. Stosz,et al.  Automated system for fingerprint authentication using pores and ridge structure , 1994, Optics & Photonics.

[2]  Josef Kittler,et al.  Introduction to the Special Issue on Biometrics: Progress and Directions , 2007, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  David Zhang,et al.  Adaptive fingerprint pore modeling and extraction , 2010, Pattern Recognit..

[4]  Anil K. Jain,et al.  Handbook of Fingerprint Recognition, Second Edition , 2009 .

[5]  Guangming Lu,et al.  Fast pore matching method based on deterministic annealing algorithm , 2017, IET Image Process..

[6]  Anil K. Jain,et al.  Pores and Ridges: High-Resolution Fingerprint Matching Using Level 3 Features , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  David Zhang,et al.  Fingerprint Pore Matching Based on Sparse Representation , 2010, 2010 20th International Conference on Pattern Recognition.

[8]  David Zhang,et al.  Direct Pore Matching for Fingerprint Recognition , 2009, ICB.

[9]  Maurício Pamplona Segundo,et al.  Dynamic Pore Filtering for Keypoint Detection Applied to Newborn Authentication , 2014, 2014 22nd International Conference on Pattern Recognition.

[10]  David Zhang,et al.  High resolution partial fingerprint alignment using pore-valley descriptors , 2010, Pattern Recognit..

[11]  Whoi-Yul Kim,et al.  Fingerprint pore matching method using polar histogram , 2014, The 18th IEEE International Symposium on Consumer Electronics (ISCE 2014).

[12]  Vincenzo Piuri,et al.  A novel pore extraction method for heterogeneous fingerprint images using Convolutional Neural Networks , 2017, Pattern Recognit. Lett..

[13]  Richa Singh,et al.  On Analysis of Rural and Urban Indian Fingerprint Images , 2010, ICEB.

[14]  Joonki Paik,et al.  Optimum Geometric Transformation and Bipartite Graph-Based Approach to Sweat Pore Matching for Biometric Identification , 2018, Symmetry.

[15]  Houda Derbel,et al.  An efficient method for the extraction of closed and open pores in fingerprint images , 2019, 2019 16th International Multi-Conference on Systems, Signals & Devices (SSD).

[16]  Fanglin Chen,et al.  Hierarchical Minutiae Matching for Fingerprint and Palmprint Identification , 2013, IEEE Transactions on Image Processing.

[17]  Yao Lu,et al.  High resolution fingerprint recognition using pore and edge descriptors , 2019, Pattern Recognit. Lett..

[18]  D. Dubois,et al.  On Possibility/Probability Transformations , 1993 .