Two-stage quality adaptive fingerprint image enhancement using Fuzzy c-means clustering based fingerprint quality analysis

Fingerprint recognition techniques are immensely dependent on quality of the fingerprint images. To improve the performance of recognition algorithm for poor quality images an efficient enhancement algorithm should be designed. Performance improvement of recognition algorithm will be more if enhancement process is adaptive to the fingerprint quality (wet, dry or normal). In this paper, a quality adaptive fingerprint enhancement algorithm is proposed. The proposed fingerprint quality assessment algorithm clusters the fingerprint images in appropriate quality class of dry, wet, normal dry, normal wet and good quality using fuzzy c-means technique. It considers seven features namely, mean, moisture, variance, uniformity, contrast, ridge valley area uniformity and ridge valley uniformity into account for clustering the fingerprint images in appropriate quality class. Fingerprint images of each quality class undergo through a two-stage fingerprint quality enhancement process. A quality adaptive preprocessing method is used as front-end before enhancing the fingerprint images with Gabor, short term Fourier transform and oriented diffusion filtering based enhancement techniques. Experimental results show improvement in the verification results for FVC2004 datasets. Significant improvement in equal error rate is observed while using quality adaptive preprocessing based approaches in comparison to the current state-of-the-art enhancement techniques.

[1]  Anil K. Jain,et al.  Latent orientation field estimation via convolutional neural network , 2015, 2015 International Conference on Biometrics (ICB).

[2]  Christoph Busch,et al.  Fingerprint skin moisture impact on biometric performance , 2015, 3rd International Workshop on Biometrics and Forensics (IWBF 2015).

[3]  Christoph Busch,et al.  Survey on the impact of fingerprint image enhancement , 2017, IET Biom..

[4]  Sameem Abdul Kareem,et al.  Fingerprint image enhancement and recognition algorithms: a survey , 2012, Neural Computing and Applications.

[5]  Christoph Busch,et al.  Finger image quality assessment features - definitions and evaluation , 2016, IET Biom..

[6]  Anil K. Jain,et al.  FVC2002: Second Fingerprint Verification Competition , 2002, Object recognition supported by user interaction for service robots.

[7]  Naixue Xiong,et al.  Two-Stage Enhancement Scheme for Low-Quality Fingerprint Images by Learning From the Images , 2013, IEEE Transactions on Human-Machine Systems.

[8]  Lawrence O'Gorman,et al.  An approach to fingerprint filter design , 1989, Pattern Recognit..

[9]  Xudong Jiang,et al.  Fingerprint image quality analysis , 2004, 2004 International Conference on Image Processing, 2004. ICIP '04..

[10]  Davide Maltoni,et al.  Fingerprint enhancement using contextual iterative filtering , 2012, 2012 5th IAPR International Conference on Biometrics (ICB).

[11]  Anil K. Jain,et al.  FVC2004: Third Fingerprint Verification Competition , 2004, ICBA.

[12]  Bart Jacobs,et al.  Biometrics and their use in e-passports , 2009, Image Vis. Comput..

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

[14]  Ju Cheng Yang,et al.  A fingerprint verification algorithm using tessellated invariant moment features , 2008, Neurocomputing.

[15]  Sung-Bae Cho,et al.  Adaptive fingerprint image enhancement with fingerprint image quality analysis , 2006, Image Vis. Comput..

[16]  Julian Fiérrez,et al.  A Comparative Study of Fingerprint Image-Quality Estimation Methods , 2007, IEEE Transactions on Information Forensics and Security.

[17]  Yu Xue,et al.  Fingerprint enhancement rooted in the spectra diffusion by the aid of the 2D adaptive Chebyshev band-pass filter with orientation-selective , 2017, Inf. Sci..

[18]  Xiaojun Jing,et al.  Low quality fingerprint image enhancement based on Gabor filter , 2010, 2010 2nd International Conference on Advanced Computer Control.

[19]  Mamdouh F. Fahmy,et al.  A fingerprint segmentation technique based on Morphological processing , 2013, IEEE International Symposium on Signal Processing and Information Technology.

[20]  Somnath Dey,et al.  Fingerprint Image Quality Assessment and Scoring , 2017, MIKE.

[21]  Babu M. Mehtre,et al.  Fingerprint image analysis for automatic identification , 1993, Machine Vision and Applications.

[22]  C. Gottschlich,et al.  Oriented diffusion filtering for enhancing low-quality fingerprint images , 2012, IET Biom..

[23]  Yu Xue,et al.  Combining Gabor filtering and classification dictionaries learning for fingerprint enhancement , 2017, IET Biom..

[24]  B. Sherlock,et al.  Fingerprint enhancement by directional Fourier filtering , 1994 .

[25]  Josef Bigün,et al.  Local Features for Enhancement and Minutiae Extraction in Fingerprints , 2008, IEEE Transactions on Image Processing.

[26]  A. J. Willis,et al.  A cost-effective fingerprint recognition system for use with low-quality prints and damaged fingertips , 2001, Pattern Recognit..

[27]  Sandip S. Patil,et al.  Wet and dry fingerprint enhancement by using multi resolution technique , 2016, 2016 International Conference on Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC).

[28]  Anil K. Jain,et al.  A Real-Time Matching System for Large Fingerprint Databases , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[29]  Vincenzo Piuri,et al.  Automatic Classification of Acquisition Problems Affecting Fingerprint Images in Automated Border Controls , 2015, 2015 IEEE Symposium Series on Computational Intelligence.

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

[31]  Anil K. Jain,et al.  Performance evaluation of fingerprint verification systems , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[32]  Charles L. Wilson,et al.  A novel approach to fingerprint image quality , 2005, IEEE International Conference on Image Processing 2005.

[33]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[34]  Anil K. Jain,et al.  FVC2000: Fingerprint Verification Competition , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[36]  Chao Zhang,et al.  A Top-Down Fingerprint Image Enhancement Method Based on Fourier Analysis , 2004, SINOBIOMETRICS.

[37]  Khaled Ahmed Nagaty.  An adaptive hybrid energy-based fingerprint matching technique , 2005, Image Vis. Comput..

[38]  Ingvar Claesson,et al.  Adaptive Fingerprint Image Enhancement With Emphasis on Preprocessing of Data , 2013, IEEE Transactions on Image Processing.

[39]  Ju Cheng Yang,et al.  Intelligent fingerprint quality analysis using online sequential extreme learning machine , 2012, Soft Computing.

[40]  Kenneth Ko,et al.  User's Guide to NIST Biometric Image Software (NBIS) , 2007 .

[41]  Vutipong Areekul,et al.  Iterative Fingerprint Enhancement with Matched Filtering and Quality Diffusion in Spatial-Frequency Domain , 2010, 2010 20th International Conference on Pattern Recognition.

[42]  Gian Luca Marcialis,et al.  Review of the Fingerprint Liveness Detection (LivDet) competition series: 2009 to 2015 , 2016, Image Vis. Comput..

[43]  Norman T. Newton.  An approach to design , 1951 .

[44]  Mayer Aladjem,et al.  Fingerprint image enhancement using filtering techniques , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[45]  Vutipong Areekul,et al.  Fingerprint Enhancement Based on Discrete Cosine Transform , 2007, ICB.

[46]  Ong Thian Song,et al.  An efficient fingerprint verification system using integrated wavelet and Fourier–Mellin invariant transform , 2004 .

[47]  Ching-Tang Hsieh,et al.  An effective algorithm for fingerprint image enhancement based on wavelet transform , 2003, Pattern Recognit..

[48]  Jian Li,et al.  Deep convolutional neural network for latent fingerprint enhancement , 2018, Signal Process. Image Commun..

[49]  Hany Kasban.  Fingerprints verification based on their spectrum , 2016, Neurocomputing.

[50]  Mubeen Ghafoor,et al.  Efficient 2-fold contextual filtering approach for fingerprint enhancement , 2014, IET Image Process..

[51]  Wei Wang,et al.  Design and implementation of Log-Gabor filter in fingerprint image enhancement , 2008, Pattern Recognit. Lett..

[52]  Muhammad Younus Javed,et al.  A hierarchical k-means clustering based fingerprint quality classification , 2012, Neurocomputing.

[53]  Vedat Tavsanoglu,et al.  Fingerprint Image Enhancement Using CNN Filtering Techniques , 2003, Int. J. Neural Syst..

[54]  David J. Kriegman,et al.  Wet fingerprint recognition: Challenges and opportunities , 2011, 2011 International Joint Conference on Biometrics (IJCB).

[55]  Xiaoou Tang,et al.  Learning a Deep Convolutional Network for Image Super-Resolution , 2014, ECCV.

[56]  Samarth Bharadwaj,et al.  Biometric quality: a review of fingerprint, iris, and face , 2014, EURASIP Journal on Image and Video Processing.

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

[58]  Carsten Gottschlich,et al.  Curved-Region-Based Ridge Frequency Estimation and Curved Gabor Filters for Fingerprint Image Enhancement , 2011, IEEE Transactions on Image Processing.