End-to-End Pore Extraction and Matching in Latent Fingerprints: Going Beyond Minutiae

Latent fingerprint recognition is not a new topic but it has attracted a lot of attention from researchers in both academia and industry over the past 50 years. With the rapid development of pattern recognition techniques, automated fingerprint identification systems (AFIS) have become more and more ubiquitous. However, most AFIS are utilized for live-scan or rolled/slap prints while only a few systems can work on latent fingerprints with reasonable accuracy. The question of whether taking higher resolution scans of latent fingerprints and their rolled/slap mate prints could help improve the identification accuracy still remains an open question in the forensic community. Because pores are one of the most reliable features besides minutiae to identify latent fingerprints, we propose an end-to-end automatic pore extraction and matching system to analyze the utility of pores in latent fingerprint identification. Hence, this paper answers two questions in the latent fingerprint domain: (i) does the incorporation of pores as level-3 features improve the system performance significantly? and (ii) does the 1,000 ppi image resolution improve the recognition results? We believe that our proposed end-to-end pore extraction and matching system will be a concrete baseline for future latent AFIS development.

[1]  Maurício Pamplona Segundo,et al.  Automatic Dataset Annotation to Learn CNN Pore Description for Fingerprint Recognition , 2018, 1809.10229.

[2]  Anil K. Jain,et al.  Automatic Latent Fingerprint Segmentation , 2018, 2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS).

[3]  Saharon Shelah,et al.  Expected Computation Time for Hamiltonian Path Problem , 1987, SIAM J. Comput..

[4]  Derek Bradley,et al.  Adaptive Thresholding using the Integral Image , 2007, J. Graph. Tools.

[5]  P. Meenen,et al.  A novel approach to fingerprint pore extraction , 2005, Proceedings of the Thirty-Seventh Southeastern Symposium on System Theory, 2005. SSST '05..

[6]  P. Pardalos,et al.  The Graph Coloring Problem: A Bibliographic Survey , 1998 .

[7]  Anil K. Jain,et al.  Infant-Prints: Fingerprints for Reducing Infant Mortality , 2019, CVPR Workshops.

[8]  Anil K. Jain,et al.  End-to-End Latent Fingerprint Search , 2018, IEEE Transactions on Information Forensics and Security.

[9]  Vincenzo Piuri,et al.  Towards touchless pore fingerprint biometrics: A neural approach , 2016, 2016 IEEE Congress on Evolutionary Computation (CEC).

[10]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[11]  Anil K. Jain,et al.  Robust Minutiae Extractor: Integrating Deep Networks and Fingerprint Domain Knowledge , 2017, 2018 International Conference on Biometrics (ICB).

[12]  R. A. Hicklin,et al.  ELFT-EFS Evaluation of Latent Fingerprint Technologies: Extended Feature Sets [Evaluation #2] , 2011 .

[13]  Maurício Pamplona Segundo,et al.  CNN-based Pore Detection and Description for High-Resolution Fingerprint Recognition , 2018, ArXiv.

[14]  Yi Chen,et al.  Dots and Incipients: Extended Features for Partial Fingerprint Matching , 2007, 2007 Biometrics Symposium.

[15]  Jean-Michel Morel,et al.  Cartoon+Texture Image Decomposition , 2011, Image Process. Line.

[16]  Yi Chen,et al.  Pores and Ridges: High-Resolution Fingerprint Matching Using Level 3 Features , 2007 .

[17]  Anil K. Jain,et al.  Fingerprints: Fixed Length Representation via Deep Networks and Domain Knowledge , 2019, ArXiv.

[18]  LinLin Shen,et al.  Feature Guided Fingerprint Pore Matching , 2017, CCBR.

[19]  Qijun Zhao,et al.  Latent Fingerprint Matching : Utility of Level 3 Features , 2010 .

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

[21]  Anil K. Jain,et al.  Latent Fingerprint Matching , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

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

[24]  Venu Govindaraju,et al.  Fingerprint enhancement using STFT analysis , 2007, Pattern Recognit..

[25]  Marc Levoy,et al.  Efficient variants of the ICP algorithm , 2001, Proceedings Third International Conference on 3-D Digital Imaging and Modeling.

[26]  J. Alex Stark,et al.  Adaptive image contrast enhancement using generalizations of histogram equalization , 2000, IEEE Trans. Image Process..

[27]  Umberto Castellani,et al.  Sparse points matching by combining 3D mesh saliency with statistical descriptors , 2008, Comput. Graph. Forum.

[28]  Tommy R. Jensen,et al.  Graph Coloring Problems , 1994 .

[29]  Heung-Kyu Lee,et al.  DeepPore: Fingerprint Pore Extraction Using Deep Convolutional Neural Networks , 2017, IEEE Signal Processing Letters.