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Fingerprint Recognition using Minutiae Extraction

Authors:
Krishna Kumar at Nanyang Technological University
Krishna Kumar
Basant Kumar at Motilal Nehru National Institute of Technology
Basant Kumar
Dharmendra Kumar at insan degree college
Dharmendra Kumar
  • insan degree college
Rachna Shah
Rachna Shah
Rachna Shah
  • This person is not on ResearchGate, or hasn't claimed this research yet.
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Abstract and Figures

Fingerprints are a great source for identification of individuals. Fingerprint recognition is one of the oldest forms of biometric identification. However recognition of fingerprint is not always easy. The objective of this paper is to provide a way for fingerprint recognition using minutiae extraction. The factors relating to obtaining high performance feature point detection algorithm, such as image quality, segmentation, image enhancement and feature detection. Commonly used features for improving fingerprint image quality are Fourier spectrum energy, Gabor filter energy and local orientation. Accurate segmentation of fingerprint ridges from noisy background is necessary. For efficient enhancement and feature extraction algorithms, the segmented features must be void of any noise.
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International Conference on ICT-Initiatives, Policies & Governance, Dehradun, India, 28-29 Nov 2011
Fingerprint Recognition using Minutiae Extraction
Krishna Kumar1, Basant Kumar2, Dharmendra Kumar3 and Rachna Shah4
1M.Tech (Student), Motilal Nehru NIT Allahabad, India, krishnanitald@gmail.com
2Assistant Prof. ECE Department, Motilal Nehru NIT Allahabad, India, singhbasant@mnnit.ac.in
3B.E, Commercial Tex Officer, Government of Uttarakhand, dharmen_mitra@yahoo.co.in
4M.Tech (Student), NIT Kurukshetra, India, rachna.shah27@gmail.com
Abstract: Fingerprints are a great source for identification of individuals. Fingerprint
recognition is one of the oldest forms of biometric identification. However recognition
of fingerprint is not always easy. The objective of this paper is to provide a way for
fingerprint recognition using minutiae extraction. The factors relating to obtaining high
performance feature point detection algorithm, such as image quality, segmentation,
image enhancement and feature detection. Commonly used features for improving
fingerprint image quality are Fourier spectrum energy, Gabor filter energy and local
orientation. Accurate segmentation of fingerprint ridges from noisy background is
necessary. For efficient enhancement and feature extraction algorithms, the
segmented features must be void of any noise.
Keywords: Image Segmentation, Minutiae, fingerprint, CN.
I. Introduction
The quality of fingerprint images and extraction of minutiae have an important role in
the performance of automatic identification and verification. In general, the minutiae
extraction algorithm starts with a preprocessing for improving the quality of images
without changing the local and global properties of the image. The fingerprint image
can be characterize by the features as core and delta or as minutiae represent the
end of ridge or the bifurcation .the methods based on minutiae are sensitive to this
stage .Any missing minutiae or false minutiae can degrade the performance of the
matching algorithm. In literature search we found many techniques for enhancement,
Hong et al proposed an effective method based on Gabor filters]. Gabor filters have
both frequency ridge and orientation ridge properties; the frequency ridge depends
on orientation ridges. Chikkerur et al proposed an efficient implementation of
contextual filtering based on short-time Fourier transform (STFT) that requires
partitioning the image into small overlapping blocks and performing Fourier analysis
separately on each block. The orientation, frequency and mask region of image are
all simultaneously estimated [1].The several approaches to automatic minutiae
extraction are two categories techniques, there are different from one other. The
most of these methods transform fingerprint images into binary images, the images
obtained are submitted to a thinning process which allows for the ridge line thickness
to be reduced to one pixel finally, a simple image scan allows for locating the pixels
that correspond to minutiae. On the other hand, other techniques are based on ridge
line, where the minutiae are extracted directly from gray images.
What is a fingerprint?
Fingerprints are the patterns formed on the epidermis of the fingertip. The fingerprints
are of three types: arch, loop and whorl. The fingerprint is composed of ridges and
International Conference on ICT-Initiatives, Policies & Governance, Dehradun, India, 28-29 Nov 2011
valleys. The interleaved pattern of ridges and valleys are the most evident structural
characteristic of a fingerprint. There are three main fingerprint features
a) Global Ridge Pattern
b) Local Ridge Detail
c) Intra Ridge Detail
Global ridge detail:
There are two types of ridge flows: the pseudo-parallel ridge flows and high-curvature
ridge flows which are located around the core point and/or delta point(s). This
representation relies on the ridge structure, global landmarks and ridge pattern
characteristics.
The commonly used global fingerprint features are:
(i) Singular points – They are discontinuities in the orientation field. There are two
types of singular points- core and delta. A core is the uppermost of a curving ridge,
and a delta point is the point where three ridge flows meet. They are used for
fingerprint registration and classification.
(ii) Ridge orientation map – They are local direction of the ridge-valley structure. It
is helpful in classification, image enhancement, and feature verification and filtering.
(iii) Ridge frequency map – They are the reciprocal of the ridge distance in the
direction perpendicular to local ridge orientation. It is used for filtering of fingerprint
images.
Local Ridge Detail:
This is the most widely used and studied fingerprint representation. Local ridge
details are the discontinuities of local ridge structure referred to as minutiae. They are
used by forensic experts to match two fingerprints. There are about 150 different
types of minutiae. Among these minutiae types, ridge ending and ridge bifurcation
are the most commonly used as all the other types of minutiae are combinations of
ridge endings and ridge bifurcations.
Fig. 1 Types of minutiae
The minutiae are relatively stable and robust to contrast, image resolutions, and
global distortion when compared to other representations. Although most of the
automatic fingerprint recognition systems are designed to use minutiae as their
fingerprint representations, the location information and the direction of a minutia
point alone are not sufficient for achieving high performance. Minutiae-derived
secondary features are used as the relative distance and radial angle are invariant
with respect to the rotation and translation of the fingerprint.
Intra Ridge Detail
On every ridge of the finger epidermis, there are many tiny sweat pores and other
permanent details. Pores are distinctive in terms of their number, position, and
shape. However, extracting pores is feasible only in high-resolution fingerprint
images and with very high image quality. Thus the cost is very high. Therefore, this
International Conference on ICT-Initiatives, Policies & Governance, Dehradun, India, 28-29 Nov 2011
kind of representation is not adopted by current automatic fingerprint identification
systems (AFIS).
Fingerprint recognition
Fingerprint recognition is one of the popular biometric techniques. It refers to the
automated method of verifying a match between two fingerprint images. It is mainly
used in the identification of a person and in criminal investigations. It is formed by the
ridge pattern of the finger. Discontinuities in the ridge pattern are used for
identification. These discontinuities are known as minutiae. For minutiae extraction
type, orientation and location of minutiae are extracted.
Two features of minutiae are used for identification: termination and bifurcation.
(a) Ridge ending (b) Bifurcation
Fig .2 Types of local ridge features
The advantages of fingerprint recognition system are
(a) They are highly universal as majority of the population have legible
fingerprints.
(b) They are very reliable as no two people (even twins) have same fingerprint.
(c) Fingerprints are formed in the fetal stage and remain structurally unchanged
throughout life.
(d) It is one of the most accurate forms of biometrics available.
(e) Fingerprint acquisition is non intrusive and hence is a good option.
Approach
There are two approaches for fingerprint recognition. They are image based
approach, texture based approach and minutiae based approach.
In image based matching, the image itself is used as the template. It requires only
low resolution images. Matching is done by optical correlation and is extremely fast. It
is based on the global features of a whole fingerprint image. However it requires
accurate alignment of the fingerprint samples and is not favorable for changes in
scale, orientation and position.
The second is the texture based approach. It uses texture information for matching
and performs well with poor quality prints. However like image based matching it
requires accurate alignment of the two prints and not invariant to translation,
orientation and non-linear distortion.
Minutiae-based approach is the last approach. Here the ridge features called
minutiae are extracted and stored in a template for matching. It is invariant to
translation, rotation and scale changes. It is however error prone in low quality
images.
The minutiae based approach is applied. Usually before minutiae extraction, image
preprocessing is performed. In our paper we have focused mainly on the
preprocessing and extraction stage. Fingerprint enhancements techniques are used
to reduce the noise and improve the clarity of ridges against valleys.
International Conference on ICT-Initiatives, Policies & Governance, Dehradun, India, 28-29 Nov 2011
The image preprocessing consists of the following stages. They are field orientation,
ridge frequency estimation, image segmentation and image enhancement thinning. It
is followed by a minutiae extraction algorithm which extracts the main minutiae
features required for matching of two samples.
II. Minutiae Extraction
This method extracts the ridge endings and bifurcations from the skeleton image by
examining the local neighborhood of each ridge pixel using a 3×3 window. The
method used for minutiae extraction is the crossing number (CN) method. This
method involves the use of the skeleton image where the ridge flow pattern is eight-
connected. The minutiae are extracted by scanning the local neighborhood of each
ridge pixel in the image using a 3×3 window. CN is defined as half the sum of the
differences between the pairs of adjacent pixel. The ridge pixel can be divided into
bifurcation, ridge ending and non-minutiae point based on it. A ridge ending point has
only one neighbor, a bifurcation point possesses more than two neighbors, and a
normal ridge pixel has two neighbors. A CN value of zero refers to an isolated point,
value of one to a ridge ending, two to a continuing ridge point, three to a bifurcation
point and a CN of four means a crossing point. Minutiae detection in a fingerprint
skeleton is implemented by scanning thinned fingerprint and counting the crossing
number. Thus the minutiae points can be extracted. A 3×3 window is used. The CN is
given by
For a pixel i, the eight pixels are scanned in an anti-clockwise direction. The pixel can
be classified after obtaining its pixel value. The coordinates, orientation of the ridge
segment and type of minutiae of each minutiae point is recorded for each minutiae.
After a successful extraction of minutiae, they are stored in a template, which may
contain the minutia position (x,y), minutia direction (angle), minutia type (bifurcation
or termination), and in some case the minutia quality may be considered. During the
enrollment the extracted template are stored in the database and will be used in the
matching process as reference template or database template. During the verification
or identification, the extracted minutiae are also stored in a template and are used as
query template during the matching.
Fig. 3 Original Image Fig. 4 Image after thinning Fig.5 Minutiae Extraction
International Conference on ICT-Initiatives, Policies & Governance, Dehradun, India, 28-29 Nov 2011
Fig.6 Minutiae Extraction and Zooming
III. Conclusion
Our proposed method provides best way for fingerprint recognition by minutiae
extraction and zooming. It also provides excellent accuracy.
References
[1] Abbad Khalid, Tairi Hamid, and Aarab Abdellah, , “Minutiae Extraction Based on
Propriety of Curvature”, International Journal of Computer Theory and
Engineering, Vol. 3, No. 3, June 2011.
[2] D. Maltoni, D. Maio, A. K. Jain, and S. Prabhakar, “Handbook of Fingerprint
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[3] Rafael C. Gonzales, Paul Wintz, “ Digital Image Processing”, 2nd Edition, Addison-
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[4] Hong, L., Wan, Y., and Jain, A. K., “Fingerprint image enhancement: Algorithm
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[4] Peter D Kovesi, MATLAB and Octave Functions for Computer Vision and Image
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[6] Sang Keun Oh, Joon Jae Lee, Chul Hyun Park, Bum Soo Kim, Kil Houm Park,
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[7] Caohong Wu, Zhixin Shi, Venu Govindaraju, “Fingerprint Image Enhancement
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[9] Sunny Arief SUDIRO, Michel PAINDAVOINE, Tb. Maulana KUSUMA, “Simple
Fingerprint Minutiae Extraction Algorithm Using Crossing Number On Valley
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[10] Sunny Arief SUDIRO, Michel PAINDAVOINE, Trini Saptariani, Rudi Trisno
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July 2007.
[11] Sunny Arief Sudiro, ‘’ Thinning Algorithm for Image Converted in Fingerprint
Recognition System’’, National Seminar of Soft Computing, Intelligent Systems &
Information Technology 2005, Petra University, Surabaya-Indonesia, July 2005.
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... Fig (7) below represents the 3×3 window. Fig. 7 3×3 window of local neighborhood ridge pixel The crossing number XY value is then computed, which is defined as half the sum of the differences between pairs of neighboring pixels ri and ri+1 [15]: XY(p, q) = 0.5 |, = A pixel is classified after obtaining its pixel value and eight pixels are scanned in an anti-clockwise direction for each pixel i [15]. After accomplishing the extraction successfully minutiae are stored in a template, which may contain the position of minutiae (p, q), direction of minutiae (θ) and type of minutiae (s). ...
... Fig (7) below represents the 3×3 window. Fig. 7 3×3 window of local neighborhood ridge pixel The crossing number XY value is then computed, which is defined as half the sum of the differences between pairs of neighboring pixels ri and ri+1 [15]: XY(p, q) = 0.5 |, = A pixel is classified after obtaining its pixel value and eight pixels are scanned in an anti-clockwise direction for each pixel i [15]. After accomplishing the extraction successfully minutiae are stored in a template, which may contain the position of minutiae (p, q), direction of minutiae (θ) and type of minutiae (s). ...
... After accomplishing the extraction successfully minutiae are stored in a template, which may contain the position of minutiae (p, q), direction of minutiae (θ) and type of minutiae (s). During the identification, these extracted minutiae are used as a query template [15]. XY properties mentioned in Table 1 ...
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Obtaining Parameter of Minutiae Points Detected by Crossing Number Algorithm on Valley Structure
  • Jul 2007
  • 26-27
  • Sunny Arief
  • Sudiro Michel
  • Trini Saptariani
  • Rudi Trisno Yuwono
Sunny Arief SUDIRO, Michel PAINDAVOINE, Trini Saptariani, Rudi Trisno Yuwono, "Obtaining Parameter of Minutiae Points Detected by Crossing Number Algorithm on Valley Structure", ICSIIT 2007, Bali-Indonesia, pp. 104-109, 26-27 July 2007.
Thinning Algorithm for Image Converted in Fingerprint Recognition System'', National Seminar of Soft Computing, Intelligent Systems & Information Technology
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Sunny Arief Sudiro, '' Thinning Algorithm for Image Converted in Fingerprint Recognition System'', National Seminar of Soft Computing, Intelligent Systems & Information Technology 2005, Petra University, Surabaya-Indonesia, July 2005.
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Sang Keun Oh, Joon Jae Lee, Chul Hyun Park, Bum Soo Kim, Kil Houm Park, "New Fingerprint Image Enhancement Using Directional Filter Bank", Journal of WSCG Vol.11 ISSN 1213-6972, 2003.