A Novel Thinning Algorithm for Fingerprint Recognition - CiteSeerX

International Journal of Engineering Sciences, 2(2) February 2013, Pages: 43-48

TI Journals ISSN 2306-6474

International Journal of Engineering Sciences www.waprogramming.com

A Novel Thinning Algorithm for Fingerprint Recognition Muzhir Shaban Al-Ani Anbar University – Anbar – Iraq. AR TIC LE INF O

AB STR AC T

Keywords:

Years ago, many algorithms are published for fingerprint recognition and these algorithms has different accuracy rate. This paper deals with the study of the existing fingerprint recognition algorithms in order to improve the performance of the proposed fingerprint algorithm to develop an efficient novel system. The proposed fingerprint algorithm is concentrated on the improvement of the thinning process. Fingerprint enhancement and minutiae extraction based on optimal thinning. The output results indicate a significant improvement of the fingerprint recognition pattern.

Fingerprint Recognition Fingerprint Thinning Fingerprint Identification Biometric Recognition

© 2013 Int. j. eng. sci. All rights reserved for TI Journals.

1.

Introduction

The term biometric comes from the Greek words bios (life) and metrikos (measure). It is well known that humans intuitively use some body characteristics such as face, eyes, hand, finger, iris, gait, or voice to recognize each other. Since, today, a wide variety of applications require reliable verification schemes to confirm the identity of an individual, recognizing humans based on their body characteristics became more interesting in emerging technologies and applications. Traditionally, passwords and ID cards have been used to restrict access to secure systems but these methods can easily be breached and are unreliable. Biometric cannot be borrowed, stolen, or forgotten, and forging one is practically impossible. Biometric identification from a print made by an impression of the ridges in the skin of a finger; often used as evidence in criminal investigations [1]. A biometric system is essentially a pattern-recognition system that recognizes a person based on a feature vector derived from a specific physiological or behavioral characteristic that the person possesses. That feature vector is usually stored in a database after being extracted. A biometric system based on a physiological characteristics is generally more reliable than one which adopts behavioral characteristics, even if the latter may be more easy to integrate within certain specific applications. Biometric system can than operate in two modes: verification or identification. While identification involves comparing the acquired biometric information against templates corresponding to all users in the database, verification involves comparison with only those templates corresponding to the claimed identity. This implies that identification and verification are two problems that should be dealt with separately [2]. Fingerprint is one of the famous used for personal identification. Fingerprint ridges are formed during the third to fourth month of fetal development. The ridges begin to develop on the skin of the thumbs and fingers. The purpose of these ridges is to give the fingers a firmer grasp and to avoid slippage. These ridges allow the fingers to grasp and pick up objects.

Figure 1. types of fingerprint patterns * Corresponding author. Email address: [email protected]

Muzhir Shaban Al-Ani

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Int ernational Journal of Engi neering Sc iences, 2(2) February 2013

The three main basic patterns of fingerprint ridges are the arch, loop, and whorl as shown in figure (1) :   

arch: The ridges enter from one side of the finger, rise in the center forming an arc, and then exit the other side of the finger. loop: The ridges enter from one side of a finger, form a curve, and then exit on that same side. whorl: Ridges form circularly around a central point on the finger.

About 65 percent of the total population has loops, 30 percent have whorls, and 5 percent have arches [3].

2.

Literature reviews

Many algorithms are proposed and implemented to recognize fingerprint, and some of them are demonstrated as below: Ali et al. proposed a method that consists of two phases; the first phase is carried out using fingerprint image enhancement and thinning, and the second phase consists of extracting minutia, ridge ending, bifurcation and all other features in order to produce initial pattern. The achieved results are discussed for security improvement. The proposed technique also shows considerable improvement in the minutia detection process in terms of both efficiency and speed [4]. Avinash Pokhriyal et. al implemented a method called MERIT (Minutiae Extraction using Rotation Invariant Thinning), as it thins a fingerprint image irrespective of the fingerprint's position and then extracts minutiae points from a fingerprint image. First of all, we binarize the fingerprint image and convert it into a 0-1 pattern. Then, we apply some morphological operations like dilation and erosion, and also some if-then rules governing a 3x3 mask that is to be convoluted throughout the image to skeletonize it. In the end, some postprocessing is done on the thinned fingerprint image to remove false minutiae structures from it [5]. Muzhir Shaban Al-Ani et. al. proposed handwritten signature recognition system is implemented via many steps such as Discrete Wavelet Transform (DWT), feature vector generation, fusion between feature vectors, then applying Support Vector Machine (SVM). The results obtained from the verification process are better than the results obtained from the identification under the same circumstances [6]. Sangita K Chaudahri combined many methods to build a minutia extractor and minutia matcher. The combination of multiple methods comes from a wide investigation into research papers. Also some novel changes like segmentation using Morphological operations, improved thinning, false minutiae removal methods, minutia marking with special considering the triple branch counting, minutia unification by decomposing a branch into three terminations, and matching in the unified x-y coordinate system after a two-step transformation are used in the work [7]. Om Preeti Chaurasia founded that if a fingerprint is processed in this particular order, the final output is good enough for minutiae detection and feature extraction. many experiments are done on fingerprint images and found that this particular order of processing was producing better result. So if the input image is good this method will produce a good output, but if image is captured using a good quality device, then this method will produce an equal quality output as in other existing techniques [8]. Sasan Golabi et al. implemented an algorithm by applying four boxes of matrices; each of them thins ridges due to a specific direction; i.e., diagonal, horizontal and vertical directions. This algorithm also is able to thin discrete Latin Characters or symbols. For evaluating the proposed method, several robust and reliable experiments have been employed and the results confirm the higher ability of the proposed method in comparison with the other competing one [9]. Ravi Kumar et al. developed a simple technique for fingerprint analysis using minutiae extraction process with the combination of several techniques for image pre-processing to improve the input image until it is suitable for minutiae extraction. In addition, two major categories of minutiae and bifurcation are used here [10]. Muzhir Shaban Al-Ani et. al. proposed a novel face recognition approach based on wavelet-curvelet technique. This algorithm based on the similarities embedded in the images that utilize the wavelet-curvelet technique to extract facial features [11].

3.

Fingerprint recognition

Archaeologists discovered fingerprints pressed into clay tablet contracts dating back to 1792–1750 b.c. in Babylon as shown in figure (2). In ancient China, it was common practice to use inked fingerprints on all official documents, such as contracts and loans. The oldest known document showing fingerprints dates from the third century b.c. Chinese historians have found finger and palm prints pressed into clay and wood writing surfaces and surmise that they were used to authenticate official seals and legal documents [12].

A Novel Thinning Algorithm for Fingerprint Recognition

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Internat ional Jour nal of Engineeri ng Science s, 2(2) Fe br uar y 2013

Figure 2. Babylon fingerprints

As soon as fingerprints were discovered to be a reliable means of identification, criminals began to devise ways to alter them so they could avoid being identified. Two important facts of fingerprint that have risen from researches and practices are: a person's fingerprint will not naturally change structure after about one year after birth and the fingerprints of individuals are unique [13]. Fingerprint recognition is one of the most well-known and popular personal identification and security, because of their uniqueness and easy to use. Fingerprint recognition or fingerprint authentication refers to the automated method of verifying a match between two human fingerprints. fingerprint recognition system is the most matured and accepted biometric system. Fingerprints are one of many forms of biometrics used to identify individuals and verify their identity. Latent fingerprints are not visible, but techniques can bring them out. Dusting surfaces such as drinking glasses, the faucets on bathroom sinks, telephones, and the like with a fine carbon powder can make a fingerprint more visible [14].

4.

The proposed fingerprint algorithm

In a fingerprint, the dark lines of the image are called the ridges and the white area between the ridges is called valleys. This work is done applying several steps to achieve our goal:     

Collect several fingerprint image for the same person. Construct a specific fingerprint database. Classify the fingerprint according to their characteristics. Construct the algorithm to recognize the pattern. Test the implemented algorithm to check its accuracy.

The construction of the implemented algorithm is done via several components as shown in figure(3).

Figure 3. components of fingerprint system

Preprocessing process Preprocessing process refers to the process of preparing the input fingerprint image to be ready for the next step of the system, in which it produces a good enough quality of output fingerprint image. Preprocessing contains of the following steps :


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      

Image acquisition. Converting the input image into gray scale. Removing the unwanted parts from the image. Image orientation into exact position. Noise removal operation in which no effect on the fingerprint pattern. Image resizing into exact size. Image enhancement.

Thinning Process Thinning process refers to the process of reducing the thickness of the lines as possible with minimum losses. This process is so important to identify the exact pattern of the fingerprint image. Fingerprint thinning process can be offered the following performance:    

The lines of output fingerprint image should be a single pixel as possible. The lines of output fingerprint image should not have any discontinuity as possible. The lines of output fingerprint image should be return to its centre pixel as possible. Eliminate all redundancies and unwanted pixels.

Feature Extraction Process Feature extraction process depends on the previous processes and it is the main part of the overall system in which it extract the required characteristic of the fingerprint pattern. Feature extraction process of fingerprint recognition system is very sensitive process and concentrated on illuminate the required characteristics of the Minutiae’s, this can be implemented via Minutiae detection and Minutiae enhancement and Minutia extraction. Minutiae, in fingerprinting terms, are the points of interest in a fingerprint, such as bifurcations and ridge endings.

5.

Results and analysis

Different fingerprint data are collected from a selective sample, also these data are collected using various types of data entry such as traditional and electronical. Some of these data are not adequate for processing, these data are enhanced via special operations to be adapted to the overall data. Figure (4) shows undefined fingerprint image procedure done for the original Image to get ,gray scale image, enhanced image, thinning image, morphological image and perimeter image. In which the last two images (e) and (f) indicated a specific indication of the pattern. The same procedure is implemented for arch fingerprint image that also gives a good performance results as shown in figure (5).

Figure 4. Undefined fingerprint image procedure

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A criteria depends on both false acceptance ratio (FAR) and false rejection ratio (FRR) in addition to Real Acceptance rate (RAR) is used to evaluate the system performance. For experimenting the proposed system, a set of random group male and female was used. Results of admitting the members of this set to a secure system were computed and presented. The evaluation criteria parameters obtained are; Real Acceptance rate (RAR) = 0.88, False Acceptance Rate (FAR) = 0.02 and False Rejection Rate (FRR) = 0.10.

Figure 5. Arch fingerprint image procedure

6.

Conclusion

Accurate personal identification is critical in wide range of applications such as national ID cards, electronic commerce, organizations and banking operations. Fingerprints are unique to an individual, and not even identical twins have identical fingerprints. Fingerprints consist of several main ridge patterns, including whorls, loops, and arches. Various human fingerprints patterns are collected using traditional and electronic devices then these patterns are converted to digital forms to be processed via the designed algorithm. Many modifications are introduced to the implemented algorithm to generate an optimal results. The implemented algorithm gives adequate results related to the other systems.

References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12]

S. Prabhakar, S. Pankanti, A. K. Jain, "Biometric Recognition: Security and Privacy Concerns", IEEE Security & Privacy, March/April 2003, pp. 33-42. A. K. Jain, A. Ross, S. Prabhakar, "An Introduction to Biometric Recognition", IEEE Trans. on Circuits and Systems for Video Technology, Vol. 14, No. 1, pp 4-19, January 2004. Sharath Pankanti, “On the Individuality of Fingerprints”, IEEE Transactions on Pattern Analysis and Machine Intellegence, vol. 24, No.8, August 2002. ALI H. A. and NE'MA B. M., “Multi-Purpose Code Generation Using Fingerprint Images”, 6th WSEAS International Conference on Information Security and Privacy, Tenerife, Spain, December 14-16, 2007 Avinash Pokhriyal et. al., “MERIT: Minutiae Extraction using Rotation Invariant Thinning”, International Journal of Engineering Science and Technology Vol. 2(7), 2010, 3225-3235. Muzhir Shaban Al-Ani et. al., “An Improved Proposed Approach for Hand Written Arabic Signature Recognition”, Advances in Computer Science and Engineering Volume 7, Number 1, 2011, Pages 25-35 Sangita K Chaudahri, “An algorithm for fingerprint enhancement & matching”, National Conference on Emerging Trends in Engineering & Technology (VNCET-30 Mar’12) Om Preeti Chaurasia, “An Approach to Fingerprint Image Pre-Processing”, I.J. Image, Graphics and Signal Processing, 2012, 6, 29-35. Sasan Golabi, Saiid Saadat, Mohammad Sadegh Helfroush, and Ashkan Tashk, “A Novel Thinning Algorithm with Fingerprint Minutiae Extraction Capability”, International Journal of Computer Theory and Engineering, Vol. 4, No. 4, August 2012 L. Ravi Kumar1, S. Sai Kumar2, J. Rajendra Prasad3, B. V. Subba Rao4, P. Ravi Prakash5 “ Fingerprint Minutia Match Using Bifurcation Technique”, S Sai Kumar et al , International Journal of Computer Science & Communication Networks,Vol 2(4), 478-486, Sep. 2012. Muzhir Shaban Al-Ani et. al., “Face Recognition Approach Based on Wavelet-Curvelet Technique”, Signal & Image Processing : An International Journal (SIPIJ) Vol.3, No.2, April 2012 Eric H. Holder, Jr. et. al. , “The Fingerprint”, 2004, U.S. Department of Justice, Office of Justice Programs, 810 Seventh Street N.W., Washington, DC 20531 https://www.fas.org/irp/eprint/fingerprint.pdf


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[13] [14]

O’Gorman L., “Overview of fingerprint verification technologies,” Elsevier Information Security Technical Report, Vol. 3, No. 1, 1998. Bhanu Bir, Tan Xuejun, Computational Algorithms for Fingerprint Recognition. USA: Kluwer Academic Publishers, 2004.

Author Muzhir Shaban Al-Ani has received Ph. D. in Computer & Communication Engineering Technology, ETSII, Valladolid University, Spain, 1994. Assistant of Dean at Al-Anbar Technical Institute (1985). Head of Electrical Department at Al-Anbar Technical Institute, Iraq (1985-1988), Head of Computer and Software Engineering Department at Al-Mustansyria University, Iraq (1997-2001), Dean of Computer Science (CS) & Information System (IS) faculty at University of Technology, Iraq (2001-2003). He joined in 15 September 2003 Electrical and Computer Engineering Department, College of Engineering, Applied Science University, Amman, Jordan, as Associated Professor. He joined in 15 September 2005 Management Information System Department, Amman Arab University, Amman, Jordan, as Associated Professor, then he joined computer science department in 15 September 2008 at the same university. He joined computer science department, Al-Al-Anbar University in 18 August 2009 until now.