Latent Fingerprint Enhancement via Multi-Scale Patch Based Sparse Representation

Latent fingerprint identification plays an important role for identifying and convicting criminals in law enforcement agencies. Latent fingerprint images are usually of poor quality with unclear ridge structure and various overlapping patterns. Although significant advances have been achieved on developing automated fingerprint identification system, it is still challenging to achieve reliable feature extraction and identification for latent fingerprints due to the poor image quality. Prior to feature extraction, fingerprint enhancement is necessary to suppress various noises, and improve the clarity of ridge structures in latent fingerprints. Motivated by the recent success of sparse representation in image denoising, this paper proposes a latent fingerprint enhancement algorithm by combining the total variation model and multiscale patch-based sparse representation. First, the total variation model is applied to decompose the latent fingerprint into cartoon and texture components. The cartoon component with most of the nonfingerprint patterns is removed as the structured noise, whereas the texture component consisting of the weak latent fingerprint is enhanced in the next stage. Second, we propose a multiscale patch-based sparse representation method for the enhancement of the texture component. Dictionaries are constructed with a set of Gabor elementary functions to capture the characteristics of fingerprint ridge structure, and multiscale patch-based sparse representation is iteratively applied to reconstruct high-quality fingerprint image. The proposed algorithm cannot only remove the overlapping structured noises, but also restore and enhance the corrupted ridge structures. In addition, we present an automatic method to segment the foreground of latent image with the sparse coefficients and orientation coherence. Experimental results and comparisons on NIST SD27 latent fingerprint database are presented to show the effectiveness of the proposed algorithm and its superiority over existing algorithms.

[1]  Qijun Zhao,et al.  Curvature and singularity driven diffusion for oriented pattern enhancement with singular points , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[2]  R. Tibshirani.  Regression Shrinkage and Selection via the Lasso , 1996 .

[3]  Anil K. Jain,et al.  Segmentation and Enhancement of Latent Fingerprints: A Coarse to Fine RidgeStructure Dictionary , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Anil K. Jain,et al.  Fingerprint Image Enhancement: Algorithm and Performance Evaluation , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Michael Elad,et al.  Dictionaries for Sparse Representation Modeling , 2010, Proceedings of the IEEE.

[6]  Nicolai Petkov,et al.  Comparison of texture features based on Gabor filters , 1999, Proceedings 10th International Conference on Image Analysis and Processing.

[7]  Anil K. Jain,et al.  Latent fingerprint enhancement via robust orientation field estimation , 2011, 2011 International Joint Conference on Biometrics (IJCB).

[8]  Alessandra Lumini,et al.  Fingerprint Classification by Directional Image Partitioning , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Anil K. Jain,et al.  Fingerprint Matching , 2010, Computer.

[10]  Manhua Liu,et al.  Fingerprint Enhancement via Sparse Representation , 2013, CCBR.

[11]  Anil K. Jain,et al.  Handbook of Fingerprint Recognition , 2005, Springer Professional Computing.

[12]  David R. Ashbaugh,et al.  Quantitative-Qualitative Friction Ridge Analysis: An Introduction to Basic and Advanced Ridgeology , 1999 .

[13]  Anil K. Jain,et al.  Orientation Field Estimation for Latent Fingerprint Enhancement , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  D. Maio,et al.  Semi-automatic enhancement of very low quality fingerprints , 2009, 2009 Proceedings of 6th International Symposium on Image and Signal Processing and Analysis.

[15]  Christophe Champod,et al.  Fingerprints and Other Ridge Skin Impressions, Second Edition , 2016 .

[16]  C.-C. Jay Kuo,et al.  Adaptive Directional Total-Variation Model for Latent Fingerprint Segmentation , 2013, IEEE Transactions on Information Forensics and Security.

[17]  Rajat Raina,et al.  Efficient sparse coding algorithms , 2006, NIPS.

[18]  Sabih H. Gerez,et al.  Segmentation of Fingerprint Images , 2001 .

[19]  Emmanuel J. Candès,et al.  Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies? , 2004, IEEE Transactions on Information Theory.

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

[21]  R. A. Hicklin,et al.  Repeatability and Reproducibility of Decisions by Latent Fingerprint Examiners , 2012, PloS one.

[22]  Wotao Yin,et al.  A comparison of three total variation based texture extraction models , 2007, J. Vis. Commun. Image Represent..

[23]  Manhua Liu,et al.  Fingerprint orientation modeling by sparse coding , 2012, 2012 5th IAPR International Conference on Biometrics (ICB).

[24]  Manhua Liu,et al.  Invariant representation of orientation fields for fingerprint indexing , 2012, Pattern Recognit..

[25]  Xudong Jiang,et al.  Fingerprint Retrieval for Identification , 2006, IEEE Transactions on Information Forensics and Security.

[26]  Michael Elad,et al.  Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries , 2006, IEEE Transactions on Image Processing.

[27]  Venu Govindaraju,et al.  Fingerprint Image Enhancement Using STFT Analysis , 2005, ICAPR.

[28]  Michael A. Saunders,et al.  Atomic Decomposition by Basis Pursuit , 1998, SIAM J. Sci. Comput..

[29]  Anil K. Jain,et al.  On latent fingerprint enhancement , 2010, Defense + Commercial Sensing.

[30]  Xudong Jiang,et al.  Efficient fingerprint search based on database clustering , 2007, Pattern Recognit..

[31]  M. Nikolova.  An Algorithm for Total Variation Minimization and Applications , 2004 .

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

[33]  C.-C. Jay Kuo,et al.  Latent fingerprint segmentation with adaptive total variation model , 2012, 2012 5th IAPR International Conference on Biometrics (ICB).