Segmentation and Enhancement of Fingerprint Images Based on Automatic Threshold Calculations

A new approach to fingerprint image segmentation and feature extraction is proposed to improve the implementation of Automated Fingerprint Identification System (AFIS) based on automatic threshold values. The process starts by partitioning the fingerprint image manually into 16 × 16 pixels blocks. For each block, a local threshold is calculated using its mean, variance and coherence. Then, statistical analysis is performed to find the optimal threshold value for each block. This threshold is then used to extract a foreground of the fingerprint image from the background. Later, the foreground is enhanced using a newly developed technique called filling-in-the-gap process to fill in the gaps in the foreground and eliminate any unwanted handwritten annotations in the image. The current method was evaluated using the NIST-14 database and showed reliable results on different quality images.

[1]  Hakil Kim,et al.  Enhancement of feature extraction for low-quality fingerprint images using stochastic resonance , 2011, Pattern Recognit. Lett..

[2]  Swarnajyoti Patra,et al.  A novel context sensitive multilevel thresholding for image segmentation , 2014, Appl. Soft Comput..

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

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

[5]  Carsten Gottschlich,et al.  Filter Design and Performance Evaluation for Fingerprint Image Segmentation , 2015, PloS one.

[6]  Agya Mishra,et al.  Fingerprint Segmentation Using Scale Vector Algorithm , 2015, 2015 Fifth International Conference on Communication Systems and Network Technologies.

[7]  Muhammad Hassan,et al.  A Review on Human Actions Recognition Using Vision Based Techniques , 2014 .

[8]  Qinmu Peng,et al.  Fingerprint Enhancement Based on Wavelet and Anisotropic Filtering , 2012, Int. J. Pattern Recognit. Artif. Intell..

[9]  Puneet Gupta,et al.  An efficient slap fingerprint segmentation and hand classification algorithm , 2014, Neurocomputing.

[10]  Bir Bhanu,et al.  Latent Fingerprint Image Segmentation Using Fractal Dimension Features and Weighted Extreme Learning Machine Ensemble , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

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

[12]  Fulufhelo Vincent Nelwamondo,et al.  Quality-Based Fingerprint Segmentation , 2012, ICIAR.

[13]  Ali Ismail Awad,et al.  Fingerprint Local Invariant Feature Extraction on GPU with CUDA , 2013, Informatica.

[14]  Vojislav Lukic,et al.  ANN based fingerprint image ROI segmentation , 2014, 2014 22nd Telecommunications Forum Telfor (TELFOR).

[15]  Ming Fu,et al.  A Novel Fingerprint Image Preprocessing Algorithm , 2013 .

[16]  Hasan Fleyeh.  Segmentation and Enhancement of Low Quality Fingerprint Images , 2016, WISE.

[17]  Muna F. Hanoon.  Contrast Fingerprint Enhancement Based on Histogram Equalization Followed By Bit Reduction of Vector Quantization , 2011 .

[18]  Hui Li,et al.  Segmentation of fingerprint images based on variance and gradient factor , 2013, International Conference on Graphic and Image Processing.

[19]  Susanta Mukhopadhyay,et al.  Fingerprint Image Segmentation Using Block-Based Statistics and Morphological Filtering , 2015 .

[20]  Anjani Pandey,et al.  Survey of Different Segmentation Method for Low Quality Fingerprint Image , 2014 .