RotationâInvariant Fingerprint Identification System

International Journal of Electronics Communication and Computer Technology (IJECCT) Volume 2 Issue 4 (July 2012)

Rotation –Invariant Fingerprint Identification System R. M.Mandi

S. S. Lokhande

Electronics and Telecommunication Dept Sinhgad Institute of Technology & Science Pune, India

Abstract— The fingerprint is the most widely used Biometric. The main focus of Intelligent Fingerprint Recognition System is to improve accuracy and efficiency. This system uses an algorithm for recovering translation parameters from two fingerprint images of the same individual that differ by Scaling, Rotation, and Transformation also known as similarity transformation or Rotation-scale-Translation (RST) Transformation. This algorithm is very useful to identify two fingerprint images of the same individual which are misaligned by small transformations such as rotation or translation or scaling. The algorithm uses minutiae based matching to compare input fingerprint image with the template fingerprint image stored in the database. The algorithm uses various standard preprocessing steps. It includes all the stages to extract the minutiae. Maximum correlation between original and sensed image is found. The proposed system is tested on the pilot database. Keywords – Correlation; Image alignment; minutiae; rotation; scaling; translation; I.

INTRODUCTION

Biometric recognition is the most important technology [1] for personal identification. Biometric focuses on physiological and behavioral characteristics. Fingerprint is the widely used behavioral characteristic because two persons should not have the same fingerprint characteristics and they [2] can‟t be altered. The traditional fingerprint identification technique involves the analysis of small unique marks present on fingerprint image known as minutiae. Among the variety of minutiae types reported in the literatures, two are most significant and in heavy usage: Ridge ending, which means the abrupt end of a ridge. Ridge bifurcation, a single ridge is divided into two ridges.There are many different proposed systems for fingerprint recognition based on image, neural and fuzzy approach. The proposed algorithm is practical for fingerprint recognition. The proposed algorithm focuses on the problem of the scaling, rotation and translation of the fingerprint image. This method is based on computational geometry algorithm. The basic idea is to extract the number of end points and bifurcation points which are collectively called as minutiae [3] points of a given fingerprint image. They are stored in the database. Then acquire the sensed image. If the acquired image is rotated or translated or scaled from the original image, both images are aligned using the rotation

ISSN:2249-7838

Electronics and Telecommunication Dept Sinhgad College of Engineering Pune, India

metrics [4]. Then minutiae matching algorithm is used to count number of matched minutiae pairs. Maximum correlation between original and sensed image is found. II.

RELATED WORK

In biometric, there are various matching methods for effectiveness of recognition results. Few older methods proposed that [5] verification system may be applied directly on grey level image. Few algorithms [6] proposed minutiae count and direct angle difference between minutiae point pair. Fingerprint verification using Gabor Co-occurrence features has been proposed by S. Arivazhagan. For fingerprint verification, Gabor Wavelet Transform (GWT) algorithm [7] is used in this approach. Euclidean distance between fingers codes is considered to match. Aliaa A.A. Youssif has proposed a fingerprint recognition system. Hybrid method [8] based on a minutiae-based and correlation based is used in this system. In conclusion, the author suggests that hybrid method can perform better than the individual method. Some recent methodologies [9] showed a global minutiae and invariant moments that used the feature extraction and matching. These features vector contained radial distance, radial angle, minutiae direction and minutiae type. According to F.P.S Falguers, A.N. Marana and J. R. FaIFalguera they represented the fusion of a minutiaebased and a ridge-based fingerprint recognition method [5]. This paper represents algorithm for recovering translation parameters from two images that differ by rotation-scaling and translation. Fingerprint matching includes two sub domains: one is fingerprint verification and other is fingerprint identification. Verification works on one to one matching and identification works on one to many matching [6]. However, the underlining principle of well defined representation of a fingerprint and matching remains the same. III.

FINGERPRINT RECOGNITION SYSTEM

The block diagram of Finger Recognition System is as shown in Figure 1. First, Input step takes on-line form the fingerprint scanner or off-line input image from database. Second, the input is passed through a number of preprocessing steps such as gray scale converting, noise reduction, binarization, and thinning.[3][4]. All these steps use the built-in functions of Matlab. Third, fingerprint recognition system is started to run. It is considered by statistics of geometry approaches. Finally, decision will be made.

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Figure 2. Ridge Bifurcation

If the central pixel is 1 and has only 1 one-value neighbor, then the central pixel is a ridge ending as shown in figure 3.[3] After applying image enhancement steps, we should get the possible termination and bifurcation features. Figure 1. Block Diagram of Fingerprint Recognition System

Preprocessing Steps The performance of feature extraction for image depends on the quality of input image. To obtain the higher accuracy performance, the input image needs to undergo several image enhancement techniques. In this system, Pre-processing steps are the most important than the other recognition system. A

B

Image Enhancement Image enhancement is the process improving the quality of a digitally stored image by manipulating the image with software. It is quite easy, for example, to make an image lighter or darker, or to increase or decrease contrast. The first step in image enhancement is to convert the input image into gray scale image [10]. This is necessary because though it image appears black and white there are 3 dimensions which are present in the normal color image. Then next step is noise reduction. For this purpose median filter is used. Median filter is useful salt and paper noise which is usually present in the fingerprint image [11]. Then next step is binarization. Binarization is the process which transforms the 8 bit gray image to a one bit image. After the binarization image contains 0 values for ridges and 1 value for furrows [12]. The next step is fingerprint image thinning. In this process the redundant pixels of ridges are just one pixel wide[13]. This is done using MATLAB‟s built in morphological thinning function. Bwmorph(binaryimage, „thin‟ Inf) IV.

FINAL MINUTIAE EXTRACTION

Now that the fingerprint image is thinned the job of minutiae extraction requires three operations [12]: 

Minutiae Marking



Minutiae Representation

A.

Minutiae Marking Now thinned image is divided into 3 x 3 pixel windows Minutia marking is now done for this each 3 x 3 pixel window. If the central pixel is 1 and has exactly 3 one-value neighbors, then the central pixel is a ridge branch. It is shown in figure 2 [3].

ISSN:2249-7838

Figure 3. Ridge Terminations.

B

Minutiae Representation Finally after extracting valid minutiae points from the fingerprint they need to be stored in some form of representation common for both ridge ending and bifurcation. There are several ways to represent the minutiae. In the current system we are storing minutiae in the form of x-coordinate and y-coordinate [2] [9]. C

Minutiae Matching Fingerprint matching has been approached from several different strategies, like image based [4] and ridge pattern matching of fingerprint representation. There are also some graph based schemes are present. This algorithm is based on point pattern matching (minutiae matching). The reason for this choice is our need to design a robust, simple and fast verification algorithm and to keep a small template size.This algorithm highlights the below key points:  

 

Data files are formed from the given fingerprints. The data files contain the XY coordinates of the detected minutiae points. Then data file is analyzed to find the shortest distances between the minutiae points. In this the nearest minutiae from each minutiae is found and the shortest distance thus found is stored for analysis [11]. The distances determined are pixel wise distances and not Euclidean distances, this gives more accuracy and a error of +2 pixels is permissible [12]. Then the two minutiae points in which the shortest distance remain the same in both the images is found out and taken as reference points IJECCT | www.ijecct.org

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International Journal of Electronics Communication and Computer Technology (IJECCT) Volume 2 Issue 4 (July 2012) 



  V.

Then these distances are used for matching. If the distances tally within permissible error limit of + 2 pixels, then the minutiae points are taken as matching points Then these distances are used for matching. If the distances tally within permissible error limit of + 2 pixels, then the minutiae points are taken as matching points The numbers of points matching are compared with a threshold to determine whether the input image is matching with database image. Correlation between two images is calculated. GEOMETRICAL SPATIAL TRANSFORMATION (RST TRANSFORMATION)

An affine transformation is any transformation that preserves co linearity (i.e., all points lying on a line initially still lie on a line after transformation) and ratios of distances (e.g., the midpoint of a line segment remains the midpoint after transformation). In this sense, affine indicates a special class of projective transformations that do not move any objects from the affine space to the plane at infinity or conversely. An affine transformation is also called an affinity. It is also called as RST transformation.

Each input image is compared with the all the database images step by step and correlation between two images is found out. The maximum correlation indicates that the input image is matching with that particular database image. VI.

RESULTS

The performance of this system is evaluated on a Pilot database. The few images are captured by using Secugen Hamster plus Optical fingerprint scanner. And remaining images are taken from the readymade database available on the internet. Three databases DB1, DB2 and DB3 are created. Each database contains 10 straight fingerprint images. Each straight image is rotated by writing a small code in Matlab. It is also translated. So database for input images contain more than 200 images which are rotated and translated versions of the original image. Few sample images present in the database are as shown in below figure 4. The figure 4 below shows the Input Image as highlighted (top left) and rest of the 10 images are generated from rotation and translation of the same Input Image.

Let us consider an image function f defined over a (w, z) co ordinate system undergoes the geometric distortion to produce an image g defined over (x,y) coordinate system. This transformation may be expressed as [14]. (x,y)= T {(w,z)}

.

(1)

A. Rotation The new coordinates of a point in the x-y plane rotated by an angle θ around the z axis can be derived through elementary trigonometry [14]. Here (x,y)= T {(w,z)}, where T is the rotation transformation applied such as x = wcos θ - z sin θ y = wsin θ + z cos θ (2) B. Scaling If the x-coordinate of each point in the plane is multiplied by a positive constant Sx then the effect of this transformation is to expand or compress each plane figure in x-direction. Here T is the scaling transformation applied such as x = Sxw y = Syz (3) C. Translation A translation is defined by a vector T = (dx, dy) and the transformation of coordinates is given by x = w + dx y = z + dy (4) In the proposed algorithm this RST transformation [15] is applied on the each input fingerprint image to see how much it is rotated or scaled or translated with respect to the database images or the original images.

ISSN:2249-7838

Figure 4. Input Image (Highligted – Top Left) and Rotated/Translated Images

Each input image selected is preprocessed. In preprocessing first gray scale conversion is performed. After this step binarization of image is done. Noise is removed using median filter. Finally, fingerprint image is thinned to get ridges one pixel thin. These preprocessing steps are common for the selected input image as well as for the original database images. After successfully running the code in Matlab 7.0 the following results are obtained as shown in figure 5 which are the screenshots in the Matlab. Figure 5 a represents the original image, figure 5 b represents gray scale image, figure 5 c represents Binarized image, figure 5 d represents thinned image and figure 5 e represents the final minutiae points In this system we have calculated both features of the fingerprint image. One is ridge terminations and other is called as ridge bifurcations. Minutiae detection is a trivial task when an ideal thinned ridge map is available. However, presence of undesired spikes and breaks present in a thinned ridge map may lead to detection of many spurious minutiae. Therefore, before minutiae detection, a smoothing procedure is applied to remove spikes and to join ridges. For each detected minutiae, IJECCT | www.ijecct.org

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the following parameters are recorded: 1) x-coordinate, 2) ycoordinate.

(a)

(b)

(c)

Figure 6(b). Minutiae points of original image.

Following TABLE 1 shows False Acceptance Rate (FAR) and False Rejection Rate (FRR) at different threshold values. (d)

( e)

Figure 5(a). Original Image (b) Gray scale image (c) Binarized Image (d) Thinned Image (e) Final minutiae

Previous system was not immune to rotation and translation. So, any rotation or translation of the finger image will invalidate the employee, even-if the same employee is registered in the database. This system will still identify in such cases. From following screenshots it is clear that if input image is rotated or translated or scaled with respect to the original database image then there is significant change in the X-Y co ordinates of minutiae. Figure 6 indicates the changes.

Summary of observations is as follows  False Reject Rate (FRR) value increases as Threshold value increases  False Accept Rate (FAR) value decreases as Threshold value increases  At Threshold value of 0.023, the FAR and FRR match-up, which is termed as Equal Error Rate TABLE I.

RESULT ANALYSIS

Threshold 1 Threshold 2 Threshold 3 Threshold 4 Threshold 5 Threshold False Accept False Accept % False Reject False Reject % Genuine Accept Accuracy %GAR

0.01 15 21.7% 0 0.0% 54 91.5%

0.017 12 17.4% 0 0.0% 54 91.5%

0.023 4 5.8% 4 5.8% 52 88.1%

0.03 1 1.4% 22 31.9% 36 61.0%

0.04 0 0.0% 29 42.0% 30 50.8%

0.45 0.4 0.35 0.3 0.25

Figure 6 (a). Minutiae points of Input image

Threshold False Accept %

0.2

False Reject %

In the above figure X and Y coordinates represent the minutiae‟s of the input fingerprint image which are either rotated or translated with respect to the original image

0.15 0.1 0.05 0

Figure 6 (b) indicates the X and Y coordinates or the original minutiae points of the database image. After this result we are taking any arbitrary point as the reference point and aligning it to the origin. We are also aligning the remaining points with respect to this new reference point. Then find the distances between the reference point and the remaining points. Then consider the shortest distance and then finally find the correlation between two images. ISSN:2249-7838

Threshold Samples

Figure 7. FAR & FRR vs. Threshold

Figure 7 shows False Acceptance Rate and False Rejection Rate obtained after detailed analysis of test database and Input database with varying threshold values


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[9]

Receiver Operating Characteristic Curve (ROC)

[10]

100.0% 80.0%

Genunie Acceptance Rate %

[11]

60.0% GAR

40.0%

[12] [13]

20.0% 0.0%

0.0%

1.4%

5.8%

17.4%

21.7%

False Acceptance Rate %

Figure 8. Receiver Operating Characteristic Curve (ROC)

Figure 8 shows Receiver Operating Characteristic Curve (ROC) obtained after detailed analysis of test database and Input database with varying threshold values

[14]

[15]

Rohit Singh(Y6400), Utkarsh Shah (Y6510), Vinay Gupta (Y6534), “Fingerprint Recognition”,Department of Computer Science & engineering Indian Institute of Technology, Kanpur 2009. S.Schuckers, A.Abhyankar, Detectingliveness in fingerprint scanner using wavelets:Results of the tset dataset,in:Proceedings of the Biometric Authentication Workshop, ECCV, May 2004, pp.100-110. M. Kaur,M.Singh,A.Girdhar and P.S.Sindhu,”Fingerprint verification system using minutiae extraction technique”,World Academy of Science,Engineering and Technology 46.pp497-502,2008. Biometrics Information Group www.biometricsinfo.org A.K.Jain, L.Hong, Ruud Bolle, “An Identity-Authentication System Using Fingerprints”, Proceedings of IEEE , Vol.85, No 9, 1997, pp.1365-1388. Mehfuza Holia, Prof.V.K.Thakar ”Image registration for recovering affine transformation using Nelder Mead Simplex method for optimization.2010. P.E.Danielsson and Q.Z.Ye, ”Rotation-Invariant Operators Applied to Enhancement of Fingerprints,”Proc.Eighth ICPR,pp.329333,Rome,1988.

VII. CONCLUSION The proposed system describes a simple statistical and geometry recognition method. The objective of this algorithm is to achieve higher accuracy percentage and to produce the related information of input image correctly from the database. The proposed system can identify the more accurate measurement for feature vectors of accuracy percentage. The objective of this system is to achieve higher accuracy percentage. This algorithm is useful for two fingerprint images of the same person which are misaligned by small transformations such as rotation or translation. This algorithm is useful for images taken from same sensor but differ by small transformations such as rotation or translation. Advantage of this system is that it is purely software based only scanner is required and hence economical. REFERENCES [1] [2] [3]

[4]

[5]

[6] [7]

[8]

D.Maltonie, D.Maio, A.K.Jain, S.Prabhakar, Handbook of Fingerprint Recognition, Springer,New York, 2003. Anil K.Jain, Ajay Kumar, “Biometrics of Next Generation: An Overview”, Springer, 2010. A.K.Jain, L.Hong and R.Bolle, ”On-line fingerprint verification” IEEE Transactions on Pattern Analysis and Machine Intelligent, Vol.19,No.4,pp302-314,1997. Mar Mar Min, Yadana Thein “Intelligent Fingerprint Recognition System by Using Geometry Approach ”, IEEE Trans.PAMI, 18(8),pp. 799-806, 2009. A.N.Marna, and A.K.Jain, “Ridge Based Fingerprint Matching Using Hough Transform”, IEEE Proceedings of the Sibgrapi, Natal, Brazil, 2005, pp.112-119. A.K.Jain, “Biometrics Authentication How Do I Know Who You Are”, Dept.of Computer Science and Engineering, Michigan State University. S.Arivazhagan, T.G.ArulFlora, L.Ganesan, “Fingerprint verification using gabor co-occurrence features”, IEEE International Conference on Computational Intelligence and Multimedia Applications, pp 281-285, 2007. F.P.S. Falguera, A.N.Marna and J.R.Falguera, “Fusion of Fingerprint recognition methods for robust human identification”, 11th IEEE International Conference on Computational Science and Engineering, pp.413-420, 2008.

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RotationâInvariant Fingerprint Identification System

RotationâInvariant Fingerprint Identification System

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