World Journal of Science and Technology 2012, 2(4):178-181 ISSN: 2231 – 2587 Available Online: www.worl djournalofscience.com
_________________________________________________________________ Proceedings of "Conference on Advances in Communication and Computing (NCACC'12)” Held at R.C.Patel Institute of Technology, Shirpur, Dist. Dhule,Maharastra,India. April 21, 2012
Fingerprint recognition using minutia matching Smital D.Patil1 and Shailaja A.Patil2 Department of Electronics and Telecommunication R.C.Patel Institute of Technology,Shirpur,Dist.Dhule.Maharashtra,India. ,
Abstract In the modern computerized world, it has become more and more important to authenticate people in a secure way. Modern applications like online banking or online shopping use techniques that depend on personal identification numbers, keys, or passwords. Nevertheless, these technologies imply the risk of data being forgotten, lost, or even stolen. Therefore biometric authentication methods promise a unique way to be able to authenticate people. A secure and confidential biometric authentication method is the utilization of fingerprints. Usually a technique called minutiae matching is used to be able to handle automatic fingerprint recognition with a computer system. Keywords: Fingerprint,minutia,Euclidean Keywords: Fingerprint,minutia,Euclidean Distance. INTRODUCTION The problem of resolving the identity of a person can be categorized into two fundamentally distinct types of problems with different inherent complexities (i) verification and (ii) recognition. Verification (authentication) refers to the problem of confirming or denying a persons claimed identity.Recognition refers to the problem of establishing a subjects identity. A reliable personal identification is critical in many daily transactions. For example, access control to physical facilities and computer privileges are becoming increasingly important to prevent their abuse. There is an increasing interest in inexpensive and reliable personal identification in many emerging civilian, commercial, and financial applications. Fingerprint recognition or fingerprint authentication refers to the automated method of verifying a match between two human fingerprints. Fingerprints are one of many forms of biometrics used to identify an individual and verify their identity. Because of their uniqueness and consistency over time, fingerprints have been used for over a century, more recently becoming automated(i.e.a biometric) due to advancement in computing capabilities. Fingerprint identification is popular because of the inherent ease in acquisition, the numerous sources (ten fingers) available for collection, and their established use and collections by law enforcement and immigration. What
an impression of the friction ridges and furrows on all parts of a finger. These ridges and furrows present good similarities in each small local window, like parallelism and average width. A fingerprint pattern is comprised of a sequence of ridges and valleys. In a fingerprint image, the ridges appear as dark lines while the valleys are the light areas between the ridges.
Fig 1. Fingerprint image from a sensor
A cut or burn to a finger does not affect the underlying ridge structure, and the original pattern will be reproduced when new skin grows. Ridges and valleys generally run parallel to each other, and their patterns can be analyzed on a global and local level. The three basic types of these singular regions are loop, delta, and whorl, examples of which are shown in Figure 2.
is a Fingerprint?
A fingerprint is the feature pattern of one finger (Figure 1). It is
*Corresponding Author Smital D.Patil Department of Electronics and Telecommunication R.C.Patel Institute of Technology,Shirpur,Dist.Dhule.Maharashtra,India.
Fig 2. Singular regions and core points
,
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However, shown by intensive research on fingerprint recognition, fingerprints are not distinguished by their ridges and
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furrows, but by features called Minutia, which are some abnormal points on the ridges (Figure 3).Among the variety of minutia types reported in literatures, two are mostly significant and in heavy usage: Ridge ending - the abrupt end of a ridge Ridge bifurcation - a single ridge that divides into two ridges
Fig 3.Basic types of minutiae What
is a Fingerprint recognition?
Fingerprint recognition (sometimes referred to as dactyloscopy) is the process of comparing questioned and known fingerprint against another fingerprint to determine if the impressions are from the same finger or not. It includes two sub-domains: one is fingerprint verification and the other is fingerprint identification (Figure 4). In addition, different from the manual approach for fingerprint recognition by experts, the fingerprint recognition here is referred as AFRS (Automatic Fingerprint Recognition System),which is program-based.
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any other existing biometric technology. LITERATURE OVERVIEW Ashwini R.Patil,Mukesh A.Zaveri in,”A Novel Approach for Fingerprint Matching using Minutiae” have proposed effective fingerprint matching algorithm based on feature extraction. They have also presented a novel fingerprint recognition technique for minutia extraction and a minutia matching.For minutia marking they considered one special false minutiae removal method.[1].Chandra Bhan Pal, Amit Kumar Singh, Nitin, Amrit Kumar Agrawal in ”An Efficient Multi Fingerprint Verification System Using Minutiae Extraction Technique” has combined many methods to build a minutia extractor and a minutia matcher.[4].H B Kekre and V A Bharadi in ”Fingerprint Core Point Detection Algorithm Using Orientation Field Based Multiple Features” have used Correlation based Fingerprint Recognition rather than detecting minutiae.[9]Mary Lourde R and Dushyant Khosla in ”Fingerprint Identification in Biometric Security Systems”proposed the issue of selection of an optimal algorithm for fingerprint matching in order to design a system that matches required specifications in performance and accuracy.[10]Chomtip Pornpanomchai Apiradee Phaisitkulwiwat in,”Fingerprint recognition by Euclidean Distance”proposed the fingerprint recognition by euclidean distance method.[6] SYSTEM ARCHITECTURE The architecture of a fingerprint-based automatic authentication system is shown in Figure 5. It consists of four components:(i)user interface (ii)system database(iii)enrollment module and (iv)authentication module.
Fig 4. Verification vs.Identification
However, in all fingerprint recognition problems,either verification(one to one matching) or identification(one to many matching), the underlining principlesof well defined representation of a fingerprint and matching remains the same. Fingerprint
as a biometric:
A smoothly flowing pattern formed by alternating crests (ridges) and troughs (valleys) on the palmar aspect of hand is called a palmprint. Formation of a palmprint depends on the initial conditions of the embryonic mesoderm from which they develop. The pattern on pulp of each terminal phalanx is considered as an individual pattern and is commonly referred to as a fingerprint. A fingerprint is believed to be unique to each person.Fingerprints of even identical twins are different.Fingerprints are one of the most mature biometric technologies and are considered legitimate proofs of evidence in courts of law all over the world. Fingerprints are, therefore, used in forensic divisions worldwide for criminal investigations. More recently, an increasing number of civilian and commercial applications are either using or actively considering to use fingerprintbased identification because of a better understanding of fingerprints as well as demonstrated matching performance than
Fig 5. System Architecture
The user interface provides mechanisms for a user to indicate his/her identity and input his/her fingerprints into the system.The system database consists of a collection of records, each of which corresponds to an authorized person that has access to the system.The task of enrollment module is to enroll persons and their fingerprints into the system database.A minutiae extraction algorithm is first applied to the fingerprint images and the minutiae patterns are extracted. The task of authentication module is to authenticate the identity of the person who intends to access the system. The person to be authenticated indicates his/her identity and places his/her finger on the fingerprint scanner; a digital image of his/her fingerprint is captured; minutiae pattern is extracted from the captured fingerprint image and fed to a matching algorithm which matches it against the persons minutiae templates stored in the system database to establish the identity. A.Techniques for fingerprint recognition:
Patil and Patil
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Fingerprint Image Enhancement: Fingerprint image enhancement is to make the image clearer for easy further operations. Since the fingerprint images acquired from sensors or other media are not assured with perfect quality, enhancement methods, for increasing the contrast between ridges and furrows and for connecting the false broken points of ridges due to insufficient amount of ink are very useful to keep a higher accuracy to fingerprint recognition.
Fingerprint Image Binarization: A locally adaptive binarization method is performed to binarize the fingerprint image. Such a named method comes from the mechanism of transforming a pixel value to 1 if the value is larger than the mean intensity value of the current block (16x16) to which the pixel belongs.
In general, only a Fingerprint Image Segmentation: Region of Interest (ROI) is useful to be recognized for each fingerprint image. The image area without effective ridges and furrows is first discarded since it only holds background information. Then the bound of the remaining effective area is sketched out since the minutia in the bounded region is confusing with those spurious minutiae that are generated when the ridges are out of the sensor.
Minutia Marking: After the fingerprint ridge thinning, marking minutia points is relatively easy. We only need that how can we found out the bifurcation and termination in thinned image. In general, for each 3x3 window, if the central pixel is 1 and has exactly 3 onevalue neighbors, then the central pixel is a ridge branch (Fig 6). If the central pixel is 1 and has only 1 one-value neighbor, then the central pixel is a ridge ending (Fig 7). C. Fingerprint Post-processing The preprocessing stage does not totally heal the fingerprint image. For example, false ridge breaks due to insufficient amount of ink and ridge cross-connections due to over inking are not totally eliminated. Actually all the earlier stages themselves occasionally introduce some artifacts which later lead to spurious minutia. Twelve types of false minutia are specified in Fig.8.
Fig 8. False minutia
Procedures to remove false minutia are: Figure 6. Minutia Extarction
1. If the distance between one bifurcation and one termination is less than D and the two minutias are in the same ridge (m1 case). Remove both of them. D is the average interridge width representing the average distance between two parallel neighboring ridges. 2. If the distance between two bifurcations is less than D and they are in the same ridge, remove the two bifurcations (m2, m3, m8, m10, m11 cases).
Figure 7. Bifurcation and Termination
To extract the ROI, a two-step method is used. The first step is block direction estimation and direction variety check, while the second is intrigued from some Morphological methods. Two Morphological operations called ’OPEN’ and ’CLOSE’ are adopted. The ’OPEN’ operation can expand images and remove peaks introduced by background noise. The ’CLOSE’ operation can shrink images and eliminate small cavities. B. Minutia Extraction Fingerprint Ridge Thinning: Thinning is the process of reducing the thickness of each line of patterns to just a single pixel width. The requirements of a good thinning algorithm with respect to a fingerprint are a) The thinned fingerprint image obtained should be of single pixel width with no discontinuities. b) Each ridge should be thinned to its centre pixel. c) Noise and singular pixels should be eliminated. d) No further removal of pixels should be possible after completion of thinning process. The built-in Morphological thinning function in MATLAB to do the thinning and after that an enhanced thinning algorithm is applied to obtain an accurately thinned image.
3. If two terminations are within a distance D and their directions are coincident with a small angle variation. And they suffice the condition that no any other termination is located between the two terminations. Then the two terminations are regarded as false minutia derived from a broken ridge and are removed (case m4, m5, m6). 4. If two terminations are located in a short ridge with length less than D, remove the two terminations (m7). 5. If a branch point has at least two neighboring branch points, which are each no further away than maximum distance threshold value and these branch points are closely connected on common line segment than remove the branch points (m12). Each minutia is completely characterized by the following parameters: 1) x-coordinate, 2) y-coordinate,and 3)orientation. RESULTS AND ANALYSIS After applying this algorithm on each fingerprint, we got the bifurcation and terminations as features. For matching we consider two fingerprint images. One is input image and other is query image. For matching purpose we have considered five persons with two fingerprints of each person.Fingerprint database has been taken
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from FVC2000. We have to see that by considering these features whether query image matches with the images in the databases or not. Two indexes are well accepted to determine the performance of a fingerprint recognition system:
FAR (False Acceptance Rate) - It measures the percent of invalid inputs which are incorrectly accepted.
FRR (False Rejection Rate) - It measures the percent of valid inputs which are incorrectly rejected. Database 10 fingerprints
Result FAR=40%
CONCLUSION Improved thinning in the present work contributes to: a) The image becomes perfectly thinned to single pixel width. b) More number of bifurcations can be detected, which were missed earlier due to the presence of erroneous pixels in the thinned image. The reliability of any fingerprint matching system strongly relies on the precision obtained in the minutia extraction process. A number of factors are deleterious to the correct location of minutia. Among them, the main factor is poor image quality. In this phase, a minutia extractor and a minutia matcher are using following concepts- segmentation using morphological operations,minutia marking by specially considering the average inter-ridge width, minutia unification by decomposing a branch into three terminations and matching in the unified x-y coordinate system after a 2-step transformation in order to increase the precision of the minutia localization process and elimination of spurious minutia with higher accuracy. REFERENCES [1] Ashwini R. Patil, Mukesh A. Zaveri,”Novel Approach for Fingerprint Matching using Minutiae”in Fourth Asia Int. Conference on Mathematical/Analytical Modelling and Computer Simulation 2010 IEEE 978-0-7695-4062-7/10. [2] Haiyun Xu, Raymond N.J.Veldhuis, Tom A.M.Kevenaar and Ton A.H.M.Akkermans,”A Fast Minutiae-Based Fingerprint Recognition System”in IEEE SYSTEMS JOURNAL,VOL.3,NO.4,DECEMBER 2009. [3] Kulwinder Singh, Kiranbir Kaur,Ashok Sardana,”Fingerprint Feature Extraction”in IJCST Vol. 2,Issue 3,September 2011. [4] Chandra Bhan Pal, Amit Kumar Singh, Nitin, Amrit Kumar
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Agrawal”An Efficient Multi Fingerprint Verification System Using Minutiae Extraction Technique”. [5] Manvjeet Kaur, Mukhwinder Singh, Akshay Girdhar, and Parvinder S.Sandhu”Fingerprint Verification System using Minutiae Extraction Technique”in World Academy of Science, Engineering and Technology 46, 2008. [6] Chomtip Pornpanomchai Apiradee Phaisitkulwiwat”Fingerprint Recognition By Euclidean Distance”in second International Conference on Computer and Network Technology 978-07695- 4042-9/10 2010 IEEE. [7] D.Simon-Zorita,J.Ortega-Garcia,S.Cruz-Llanas and J.Gonzalez Rodriguez,”Minutiae Extraction Scheme For Fingerprint Recognition Systems”0-7803-6725-1/01/2001 IEEE. [8] Ujjal Kumar Bhowmik,Ashkan Ashrafi,Reza R. Adhami,”A Fingerprint Verification Algorithm Using the Smallest Minimum Sum of Closest Euclidean Distance”978-0-7695-3587-6 2009 IEEE. [9] H B Kekre and V A Bharadi in ”Fingerprint Core Point Detection Algorithm Using Orientation Field Based Multiple Features”in International Conference on Advances in Computing, Control, and Telecommunication Technologies 2009 Volume 1 No.15 IEEE. [10] Mary Lourde R and Dushyant Khosla ”Fingerprint Identification in Biometric Security Systems”in International Journal of Computer and Electrical Engineering, Vol.2, No.5,October,2010. [11] Navrit Kaur Johal, Prof. Amit Kamra,”A Novel Method for Fingerprint Core Point Detection”in International Journal of Scientific and Engineering Research Volume 2,Issue 4,April2011 ISSN 2229-5518). [12] Ramandeep Kaur, Parvinder S. Sandhu and AmitKamra,”A Novel Method For Fingerprint Feature Extraction”in International Conference on Networking and Information Technology, 2010 IEEE. [13] A.Afsar,M. Arif and M. Hussain”Fingerprint Identification and Verification System using Minutiae Matching”in National Conference on Emerging Technologies 2004. [14] “Minutiae Detection algorithm for fingerprint Recognition” IEEE AESS systems magazine,2004. [15] http://bias.csr.unibo.it/FVC2000/download.asp [16] www.mathworks.com
