Fingerprint Synthesis towards constructing an Enhanced Authentication System using low resolution Webcam by mdrajibulislam

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									International Conference on Data Management (ICDM2008), IMT Ghaziabad, India, Feb. 25-26, 2008

Fingerprint Synthesis towards constructing an Enhanced Authentication System using low resolution Webcam Md. Rajibul Islam, Md. Shohel Sayeed, Andrews Samraj Faculty of Information Science and Technology (FIST) Multimedia University, Jalan Ayer Keroh lama, 75450 Melaka, Malaysia E-mail: {md.rajibul.islam05, shohel.sayeed, andrews.samraj}@mmu.edu.my Abstract Synthesis of several biometrics to improve the authentication performance has received significant attention over recent years due to a rising demand for reliable user identity authentication system. In this paper we present an approach to synthesis same biometrics because during capturing fingerprint it is not confirm that all minutiae of the whole fingerprint will be obtained in every ingestion. It is very essential to obtain whole minutiae from a fingerprint when we are using minutiae matching technique for fingerprint authentication. This paper introduces an Enhanced Authentication System using fingerprint synthesis by Projection Incorporated Subspace based on PCA (Principal Component Analysis). We have used a low resolution webcam to capture fingerprint images. So, an improved fingerprint image enhancement approach is presented. In the proposed approach, the traditional minutiae detection technique has been improved using fingerprint synthesis by Projection Incorporated Subspace based on PCA. The paper presents an analysis of why fingerprint synthesis approach is well suited for fingerprint enrolment, especially for the performance improvement of the minutiae extraction algorithm. Experimental results show that our approach is insensate to low resolution as well as with effectual performance and very effective for minutiae detection to improve authentication system. Key words: webcam, fingerprint, Projection Incorporated Subspace, Principal Component Analysis, fingerprint authentication. 1.0 INTRODUCTION Biometrics has been recognized as obligatory means to accomplish security in various areas of social life. Various kinds of biometrics are available which are being used for personal identification and various types of sensors too available in the market [1]. Among them fingerprint is the most commonly used, because of higher performance and lower cost than other biometrics. It is very significant to acquire good quality images but in practice a significant percentage of acquired images are of poor quality due to some environmental factors or user’s body condition [2]. The performance of a fingerprint image feature extraction algorithm depends heavily on the quality of the input fingerprint images. Robust fingerprint minutiae extraction systems impose computational requirements that are difficult to fulfill for a processing system [3]. Therefore, a novel approach is necessary to increase the performance of the minutiae extraction algorithm. In this paper, we have presented a subspace method based on Principle Component Analysis which incorporates the projection information of the fingerprint images. This method uses a subspace of lower dimension than that used by PCA. Also, its correct recognition rates are superior to PCA. The rest of this paper is structured as, section 2 briefly introduces the Preprocessing phase, section 3 briefly introduces the Projection Incorporated Subspace and PCA, section 4 the region merging technique (assume that section 3 and section 4 are the

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proposed approach), section 5 presents some experimental results. Finally, section 6 concludes this article. 2.0 PREPROCESSING PHASE Because of we use a low resolution webcam we have to construct a system to enhance the fingerprint image adequately. In this work we call this “Preprocessing Phase”, because we include this technique with the conventional image enhancement technique to obtain a precise fingerprint image from the fingerprint which is captured by a low resolution webcam and to improve the performance of feature extraction. In this preprocessing system it captures a colorful low resolution fingerprint image and converts it to grayscale image and performs the gamma manipulation and gamma correction to adjust lightness and intensities of the fingerprint image (Fig. 1). And then feeds the fingerprint image into a conventional approach to enhance and store the precise fingerprint image through normalization, orientation, frequencies calculation, contextual filtering and then binarisation and masking (Fig. 2).

Grayscale conversion

Fig. 1: Preprocessing technique using gamma

Gamma manipulation & gamma manipulation and gamma correction

correction.

(e)

(f)

Fig. 2: (a) Orientations overlaid, (b) Frequency data, (c) Gabor Filtered image, (d) Binary image, (e) Reliability of orientations, (f) Masked binary image.

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International Conference on Data Management (ICDM2008), IMT Ghaziabad, India, Feb. 25-26, 2008

3.0 PROJECTION INCORPORATED SUBSPACE Wu Jianxin, Chen Zhaoqian and Zhon Zhihua have described about Projection Incorporated Subspace in [4]. The reason we use the projection incorporated subspace method that it requires less eigenvectors. Using fewer eigenvectors means that fewer computational power and processing time is needed. In real-world applications the fingerprint database may have thousands of individuals or even more, therefore the saving of computational cost may be quite significant. Suppose P(x, y) be an intensity image of size N1×N2 , satisfies x∈[1, N1 ] , y∈[1,N2] , P(x, y)∈[0,1]. The vertical and horizontal integral projections are defined respectively as:
V p (x) =

∑
y =1

N2

P ( x, y)

…………(1)

H p ( y) =

∑ P ( x , y ) ……………..(2)
x =1

N1

Define the projection map Mp (x, y) for P(x, y) as v p (x)H p ( y) M p (x, y) = ………………(3) N N P
1 2

in which P is the image’s mean intensity, defined as
P =

∑

N1 x =1

∑

N

2

y =1

P (x, y)

N1N 2

………………(4)

Then, a projection incorporated version of P(x, y) is defined as

Pα ( x , y ) =

p ( x, y ) + α M p ( x, y ) 1+α

………………..(5)

in which α is called combine parameter. Since Pα (x, y) may go out of [0, 1], exhibit of the fingerprint image may be imprecise although the recognition results will not be affected using projection map. For better display, the fingerprint image in Fig. 1 has been adjusted according to: P ( x, y ) − min P ( x, y ) α α P ′ ( x, y ) = α max P ( x, y ) − min Pa ( x, y ) ………………(6) α In the above process, PCA are performed on the projection incorporated version of the fingerprint image instead of on the original image. We call the subspace find by this technique projection incorporated subspace method. And all the subspaces are shown by some boundary boxes (see Fig. 3).

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International Conference on Data Management (ICDM2008), IMT Ghaziabad, India, Feb. 25-26, 2008

(m)

(n)

Fig. 3: Fingerprint image using projection incorporated subspace method based on PCA. Image (n) is the enhance part of image (m). 3.1 Principal Component Analysis (PCA) PCA is a useful statistical technique that has found application in fields such as fingerprint recognition and image compression, and is a common technique for finding patterns in data of high dimension [5]. The main effect of PCA is dimensionality reduction, that is, mapping the ndimensional vector x into an m-dimensional space, where m<<n. The vector x can be approximated as a linear combination of a set of orthonormal vectors ui,

ˆ X = ∑ z i u i ………………..(7)
i =1

m

4.0 REGION MERGING The most natural method of region growing is to begin the growth in the raw image data, each pixel representing a single region. These regions almost certainly do not satisfy the condition of equation, and so regions will be merged as long as equation remains satisfied [6]. Region Merging outlines: 1. Define some starting method to segment the image into many small regions satisfying condition. 2. Define a criterion for merging two adjacent regions. 3. Merge all adjacent regions satisfying the merging criterion. If no two regions can be merged maintaining condition, stop. This algorithm represents a general approach to region merging segmentation. Specific methods differ in the definition of the starting segmentation and in the criterion for merging. The result of region merging usually depends on the order in which regions are merged, meaning that segmentation results will probably differ if segmentation begins, for instance, in the upper left or lower right corner. This is because the merging order can cause two similar adjacent regions R1 and R2 not to be merged, since an earlier merge used R1 and its new characteristics no longer allow it to merge with region R2 . If the merging process used a different order, this merge may have been realized. The simplest methods begin merging by starting the segmentation using regions of 2 × 2 , 4 × 4 or 8 × 8 . Region descriptions are then based on their statistical gray-level properties -- a region gray-level histogram is a good example. A region description is compared

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International Conference on Data Management (ICDM2008), IMT Ghaziabad, India, Feb. 25-26, 2008

with the description of an adjacent region; if they match, they are merged into a larger region and a new region description is computed. Otherwise, regions are marked as non-matching. Merging of adjacent regions continues between all neighbors, including newly formed ones. If a region cannot be merged with any of its neighbors, it is marked `final'. The merging process stops when all image regions are so marked. In this section, we briefly present region merging technique researched by Hyun geun yu [7]. Here a region merging post-processing phase is elucidated to reimburse for the oversegmentation problem. The most natural method to overcome the over-segmentation of watersheds transformation is to merge the small regions in a homogeneous region since they may possess certain homogeneous characteristics in intensity, texture or statistical properties. An alternative solution to the problem is to treat it as a set of potentially inconsistent constraints [6]:  − PAB  M AB = round   2πN  ……………….(8)   AB  The problem is then one of finding the set of consistent constraints which lead to a low (ideally minimal) cost. Let K AB = − PAB /(2πN AB ) Consider the difference in cost between M AB = L AB = round ( K AB ) and M AB = L AB ± 1 . The cost difference is: 1  ∆C AB = 8π 2 N AB  ± ( K AB − L AB )  …………………(9) 2   1 1 Since, by definition of rounding, − ≤ K AB − L AB ≤ , then ∆C AB ≥ 0 for both cases, 2 2 confirming that M AB = L AB is (locally) the best solution. The smallest cost difference is: 1  ∆C AB = 8π 2 N AB  − K AB − L AB  …………………..(10) 2 
Choose the interface where this cost difference is the largest. That is, choose the pair of regions where getting the offset ``wrong'' is the most disastrous. Therefore, select the pair of regions according to:
AB = arg max ∆C RS ……………(11)
RS

Once this pair is selected, merge them into a new, single region Q using the ``optimal'' offset L AB Update the statistics for all interfaces to this new region. That is, for all regions W , the new statistics are: N QW = N AW + N BW …………….(12) PQW = PAW + PBW …………….(13) From these, calculate K QW , LQW and then ∆C QW and then repeat the selection process to choose a new pair of regions to merge.

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International Conference on Data Management (ICDM2008), IMT Ghaziabad, India, Feb. 25-26, 2008

Keep merging regions until there are no more interfaces between regions (usually when only one region remains if there is a single connected set of regions initially). As each iteration of this algorithm merges two regions, the number of iterations required is N-1 Within each iteration a search for the maximum ∆C value must take place, followed by the updating or deletion of all constraints related to the two selected regions.

4.1 Region dissimilarity function Similarity between two regions can be simply described by the difference of statistical properties like average, variation or both of intensity values for each region. In this paper, the dissimilarity function defined as the equation below is used. This objective cost function is the square error of the piecewise constant approximation of the observed image, which yields a measure of the * approximation accuracy and is defined over the space of partitions. If RM is the optimal Mpartition with respect to the squared error, then the optimal (M -1) -partition is generated by * merging the pair of regions of RM , which minimizes the dissimilarity function.

δ (R

*i M

,R

*j M

) =

* * R Mi • R MJ

R

*i M

+ R

*j M

* * [ µ ( R Mi ) − µ ( R M j )] 2 I ( i , j ) …………(14)

Where

I ( i , j ) = 1, ∞ , +

{

If regions otherwise

∗ R Mi

∗ R Mj

are adjacent

According to the above formulation, the most similar pair of regions is the one minimizing square error. The determination of the optimal number of segments K* is performed by checking the value of δ (•, •). If δ is greater than a certain threshold, then the merging process is terminated. This threshold value can be obtained through hypothesis testing on noise distribution; however, the desired number of regions can be simply given to stop the merging process if the threshold value is not certain. In our experimental result shows the effect of region merging. The original image is processed with the watersheds transformation and region merging is applied to reduce the number of regions. In this case, the program stops the merging process if the average intensity difference between the optimal pair of regions being merged is greater than 12.

5.0 EXPERIMENTAL RESULTS The principle of this research is to get the whole fingerprint image accurately for enrolment or store in the database and make them more suitable for the minutiae extraction algorithm. Especially during the enhancement from a poor fingerprint image if we fail to enhance some parts of a fingerprint image, we’ll definitely fail to get some minutiae information. By using our proposed approach we can overcome this problem. The ultimate criterion for evaluating such a proposed approach is the total amount of “quality” improvement when the experiment is applied to the poor input fingerprint images. Such an improvement can be assessed subjectively by a visual inspection of a number of typical experiment results. However, a precise consistent characterization of the quality improvement is beyond the capability of subjective evaluation. Examples of the experiment results are shown in Fig. 4.1 and Fig. 4.2.
To test the proposed approach, we use the webcam database. From this dataset, we randomly selected a total of 100 fingerprints, three impressions per finger. From our first session the

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International Conference on Data Management (ICDM2008), IMT Ghaziabad, India, Feb. 25-26, 2008

results after implementation the preprocessing phase system and the conventional image enhancement approach presented by Hong, L., Wan, Y., and Jain, A. K.[3] are shown in Fig. 4.0(g)(h)(i). We used Peter Kovesi’s implementation [8] for this purpose. In our first experiment, only those images taken during the first session were used. The results obtained using the proposed Projection Incorporated subspace method based on PCA approaches are shown in Fig. 4.1 (j)(k)(l).

(g) (h) (i) Fig. 4.0: (g), (h), (i) are the enhanced fingerprints taken after performed preprocessing phase with conventional image enhancement.

(j) (k) (l) Fig. 4.1: Fingerprints which are obtained through proposed approach (PCA Subspace method).
After first experiment we combine all the subspaces by sorting and reconstruction but when we remove the boundary boxes from the fingerprint image we have seen some imperfect reconstruction which are shown by circles in Fig. 4.2(n) below. Then we use region merging technique to sort and reconstruct perfectly and got good results (see Fig. 4.2(o)(p)).

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International Conference on Data Management (ICDM2008), IMT Ghaziabad, India, Feb. 25-26, 2008

(m)

(n)

(o)

(p)

Fig. 4.2: (m) – sort and reconstructed fingerprint image with all boundary boxes, (n)-sort and reconstructed fingerprint image without boundary boxes and imperfect reconstructions are shown by the circles, (o)- merged fingerprint image using Region Merging on fingerprint image. (p)- enhance part after implementing region merging to solve imperfect reconstruction problem. Basically we evaluate our experimental results by the rate of detecting minutiae specially the ridge-ending and bifurcation and matching in the fingerprint image. We access and analyze here three fingerprints impressions from a same finger after enhanced them only and the fingerprint which has obtained using the proposed approach. Experimental results are recorded in terms of two classes, namely True and False. The True class is subdivided into True Acceptance (TA), i.e. correctly classified minutiae, Minutiae Detection rate (MDR), i.e. how many ridge ending and bifurcation detected and True Rejection (TR), and i.e. missed minutiae. The False class is subdivided into False Acceptance(FA), i.e. wrong minutiae and False Rejection (FR), i.e. correctly rejected pixels. Only TA, TR and FA are tabulated in the results (see Table 1).

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International Conference on Data Management (ICDM2008), IMT Ghaziabad, India, Feb. 25-26, 2008

Table 1: Averaged result with and without using proposed approach Fingerprint Image TA MDR TR FA Matched (%) (%) (%) TA (%) No. % Ridge80.1 83.5 9.7 89 0.39 93.5 1 ending Bifurcation 53.2 69.7 32.8 51 0.24 90.2 Ridge87.0 78.3 16.0 91 0.37 89.9 2 ending Bifurcation 52.7 59.9 41.3 58 0.27 89.7 Ridge82.9 82.1 11.5 89 0.39 91.9 3 ending Bifurcation 62.3 69.2 37.4 52 0.25 89.3 Ridge89.8 90.4 7.7 78 0.37 95.2 4 (obtained ending by Bifurcation 68.4 78.7 35.6 49 0.21 93.5 proposed approach) The averaged matched true acceptance for all the fingerprint impressions is around 90%. But we obtained quite good TA and MDR from the fingerprint image using our proposed approach. Because all fingerprints are taken from one finger but different impressions and we have combine three impressions of the same fingerprint image to get all the ridge ending and bifurcation which might be impossible to get from one impression. And using our proposed approach we obtained better result which is however still considered quite high.

6.0 CONCLUSION
We have presented a novel approach in this paper for the precise fingerprint enrolment and to improve the performance of the minutiae extraction algorithm. Our proposed approach is mainly Projection Incorporated Subspace based on PCA to combine some different impressions of a fingerprint image which has taken from a same finger. Because of webcam images are low resolution, we used preprocessing phase with the conventional image enhancement approach to obtain well enhanced fingerprint images and for the experiment using our proposed approach we have taken actually those fingerprint impressions. Assume that every three impressions we have taken strictly from a same finger. It is very hard to get good feature data from a poor fingerprint such as a fingerprint captured by webcam. We have presented this approach to overcome this problem. Therefore to obtain accurate minutiae information from an individual finger we have presented the proposed approach. In this experiment we obtained a better result and got a better TA, MDR and Matched TA from the fingerprint image using our proposed approach which has described in Table 1.

REFERENCES
[1] H. Kang, B. Lee, H. Kim, D. Shin, J. Kim, “A study on performance evaluation of fingerprint sensors”, The 4th International Conference Audio- and Video-based Biometric Person Authentication (AVBPA 2003), 2003, pp. 574–583. [2] L.C. Jain, U. Halici, I. Hayashi, S.B. Lee, S. Tsutsui, Intelligent Biometric Techniques in Fingerprint and Face Recognition, The CRC Press, 1999.

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International Conference on Data Management (ICDM2008), IMT Ghaziabad, India, Feb. 25-26, 2008

[3] Hong, L., Wan, Y., and Jain, A. K. “Fingerprint image enhancement: Algorithm and performance evaluation”. IEEE Transactions on Pattern Analysis and Machine Intelligence 20, 8 (1998), pp. 777-789. [4] Wu Jianxin, Chen Zhaoqian and Zhon Zhihua. “Projection incorporated subspace method for face recognition”. The 6th International Conference for Yong Computer Scientists, Hangzhou, China, 2001, vol.2, pp. 962-965. [5] Andrews, S., Palaniappan, R., and Asirvadam, V.S., “Single Trial Source Separation of VEP Signals using Selective Principal Components,” Proc. 2nd International Conference on Advances in Medical Signal and Information Processing, pp. 51-57, 5-8 September 2004. [6] Ming Jiang, Digital Image Processing. http://iria.pku.edu.cn/~jiangm/courses/dip/html/dip.html [7] Hyun geun yu. Morphological image segmentation for co-aligned multiple images using watersheds transformation. A thesis submitted to the Department of electrical and computer engineering at The Florida State University, chapter 5, 2004. [8] P.D. Kovesi. Matlab functions for computer vision and image analysis. http://www.csse.uwa.edu.au/~pk/Research/MatlabFns/index.html.

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