Extraction of Features From Fusion of Two Fingerprints For Security
Miss Swapnali V Mandle Mr.S S Bidwai.
M.E(E&TC)ADCTE Ashta Asst.professor,ADCTEAshta.
Dept.ofE&Tc Dept.ofE&Tc
Abstract-Biometric fingerprint recognition is considered as one of the most reliable technologies and has been extensively used in personal identification. in conventional fingerprint recognition system minutiae position are extracted and stored in the database, but from that minutiae position fake fingerprint can be generated so this is the main drawback of existing systems. To overcome this drawback if two different fingerprints fusion are used then fake fingerprint problem can be partially solved. We proposed new system that is two different fingerprints fusion for security. In this proposed system first fusion of fingerprint take place and then extraction of features like finding of minutia position or estimation of orientation that is combined template, which will be stored in database. For testing required two query fingers .Again same process is requiredas mention above for generation of combined template, which will be compare with stored database.
Keywords: fingerprint, image enhancement, filtering, minutiae extraction, orientation, combined template.
I Introduction:
In conventional method of fingerprint recognition system fingerprint enhancement is used, and using this technique minutiae positions are extracted and orientations are estimated. But if someone knows about minutiae positions database, it is easy find original image of fingerprint by using reconstruction technique of fingerprint from minutiae positions. So drawback of this system can be minimized using fusion of two different fingerprints. In this proposed system first fusion of two different fingerprint[1]-[5] take place then extraction of features like minutia positions or orientations[6] ,which will be stored into database. This is enrollment phase. In authentication phase ,takes two query fingerprints for testing. Again fusion take place and features are extracted, which will compare with stored database. Result is in the form of matching or not matching.This can be represented using following block diagram fig 1.
Fig1 A) Enrollment phase
Fig1B)Authentication phase
II. THE PROPOSED FINGERPRINT PRIVACY PROTECTION SYSTEM
In this proposed system MATLAB software is used. Using fingerprint scanner fingerprints are scanned. In this proposed system two different fingerprints are required. The proposed system includes two phases. Enrollment phase & Authentication phase.
In enrollment phase, take different two fingerprints, using this fingerprints fusion take place. After that extract the features using enhancement algorithm. Extracted features are stored in database. Working diagram of enrollment phase is as follows:
Fig 2.working diagram of Enrollment phase.
In Authentication phase, take different query fingerprints, using this fingerprints fusion take place. After that extract the features using enhancement algorithm.
Fig3. working diagram of Authentication phase.
Extracted features are comparing with stored database. If database is match then successful authentication. If database is not matched then unsuccessful authentication. Working diagram of enrollment phase is as above in fig 3:
1.Fingerprint Image acquisition
Image acquisition is the first step in the approach. It is very important as the quality of the fingerprint image must be good and free from any noise. A good fingerprint image is desirable for better performance of the fingerprint algorithms. Based on the mode of acquisition, a fingerprint image may be classified as off-line or live-scan[7][5].
An off-line image is typically obtained by smearing ink on the fingertip and creating an inked impression of the fingertip on paper. A live-scan image, on the other hand, is acquired by sensing the tip of the finger directly, using a sensor that is capable of digitizing the fingerprint on contact. Live-scan is done using sensors. There are three basic types of sensors used. They are optical sensors, ultrasonic sensors and capacitance sensors.[5][7].
Optical sensors capture a digital image of the fingerprint. The light reflected from the finger passes through a phosphor layer to an array of pixels which captures a visual image of the fingerprint. Ultrasonic sensors use very high frequency sound waves to penetrate the epidermal layer of skin.
2 Fusion of fingerprints
After scanning fingerprints image is mixed[1][2].Result of fused image is as follows:
Fig.4 Result of Fused image.
Above fig 4 shows result of fused two different fingerprints.
3 Extraction of features of fingerprints.
After fusion features are extracted, like minutiae positions or orientations. That mixed features are stored into database.For extraction of minutiae points enhancement algorithm is used[6][10].
3.1Minutiae Extraction
The most commonly employed method of minutiae extraction is the Crossing Number (CN) concept [9]. 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 window. The CN value is then computed, which is defined as half the sum of the differences between pairs of adjacent pixels in the eight-neighborhood. The ridge pixel can then be classified as a ridge ending, bifurcation or non-minutiae point. For example, a ridge pixel with a CN of one corresponds to a ridge ending, and a CN of three corresponds to a bifurcation. The Crossing Number (CN) method is used to perform 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 CN for a ridge pixel P is given by[9][10].
wherePi is the pixel value in the neighborhood of P. For a pixel P, its eight neighboring pixels are scanned in an anti-clockwise direction as follows:
P4 / P3 / P2P5 / P / P1
P6 / P7 / P8
Fig 3.scanned pixels.
After the CN for a ridge pixel has been computed, the pixel can then be
classified according to the property of its CN value. As shown in Figure 3.1, a ridge pixel with a CN of one corresponds to a ridge ending, and a CN of three corresponds to a bifurcation. For each extracted minutiae point, the following information is recorded:
- x and y coordinates,
- orientation of the associated ridge segment, and
- type of minutiae (ridge ending or bifurcation).
Fig3.1 (a) CN = 1(b) CN = 3
Above figure Examples of a ridge ending and bifurcation pixel. (a) A Crossing Number of one corresponds to a ridge ending pixel. (b) A Crossing Number of three corresponds to a bifurcation pixel. Minutiae extraction results are follows.
Fig 4 Result of Minutiae position
For finding orientations first find mean and variance after that normalization of image and then orientation.
A gray-level fingerprint image, I is defined as an N × N matrix, where I(i, j) represents the intensity of the pixel at the ith row and jth column. We assume that all the images are scanned at a resolution of 500 dots per inch(dpi). The mean and variance of a gray-level fingerprint image, I are defined as
An orientation image, O, is defined as an N ×N image, where O(i, j) represents the local ridge orientation at pixel (i, j). Local ridge orientation is usually specified for a block rather than at every pixel; an image is divided into a set of w × w non overlapping blocks and a single local ridge orientation is defined for each block. Note that in a fingerprint image, there is no difference between a local ridge orientation of 90oand 270o, since the ridges oriented at 90oand the ridges oriented at 270oin a
local neighborhood cannot be differentiated from each other.
3.2 Normalization
-Let I(i, j) denote the gray-level value at pixel (i, j), M and VAR denote the estimated mean and variance of I, respectively ,and G(i, j) denote the normalized gray-level value at pixel (i, j). The normalized image is defined as follows[6][7]:
G(i,j)
The main purpose of normalization is to reduce the variations in gray-level values along ridges and valleys.
3.3 Orientation Image
The orientation image represents an intrinsic property of the fingerprint images and defines invariant coordinates for ridges and valleys in a local neighborhood.
Given a normalized image, G, the main steps of the algorithm[6,7] are as follows:
1)DivideG into blocks of size w ×w (16 × 16).
2)Compute the gradients ∂x(i, j) and ∂y(i, j) at each pixel ( i , j).
3)Estimate the local orientation of each block centered at pixel (i, j) using the following equations:
4. Simulation results
Database successfully created result is as follows:
Fig 5 Snapshot of database created successfully
In authentication, if same fingerprints are used in same sequence then get result, successful authentication.If different fingerprints are used in different sequence then get result, un- successful authentication.
Fig 5 Snapshot ofsuccessfullyauthentication
Conclusion -
In this paper use enhancement algorithm for extracting the features. In this algorithm normalization, thinning, binarisation is used. Using this method finding minutiae extraction and finding orientations are easy. Combination of fingerprints and then extraction of features can make virtual identity. Using combination of fingerprints we can make virtual identity, and that can be used for authentication.
References:-
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