Embedding, Extracting and Matching of Fingerprint Images using Digital Watermarking Ram Charan Kesireddi
Gaurav Raj
Mtech Computer Science Engineering Amity University, Noida, UP
Assistant Professor Computer Science Engineering Amity University, Noida, UP
[email protected]
[email protected]
ABSTRACT The conventional digital watermarking scheme uses an arbitrary digital pattern as the watermark which has limitations in proving ownership of the watermark. The issue of ownership watermark is addressed. The bio-metric pattern of fingerprint is used to generate the digital watermark that has a stamp of ownership. The generated watermark has been studied for uniqueness and identification and has been used to watermark digital images. Discrete cosine transformation is used for embedding the watermark in the image.
filter for individual block in an image. Here we are using average, median and wiener filtering over the fingerprint image. In this filtering concept we are taking a normal fingerprint image and adding noise to it and removing the noise using the filters. Wiener filter is the effective filter which will remove Gaussian noise in the image [5].
Keywords Matlab, Preprocessing, DCT, Embedding, Extracting.
1. INTRODUCTION Now a day, protecting the digital media has become very tough task. Many people are trying copy the information without any access. To protect and giving security to that information the watermarking technique is been introduced. To provide more security to the data we can use robust techniques in watermarking. So that the information will be more secure. Digital watermarking is a technique used to watermark on digital data such 2D images, videos, audios, etc. It is used to show the identity of the owners and verify the authenticity and integrity of the image. Finally, the robustness is considered as the ability of extracting the hidden data from the watermarked signal as well as the survival of the watermark after manipulations or attacks. Because of various operations on digital signal, no watermarking scheme is perfectly robust. As usual, each approach can be robustness against to some given and limited alterations. Even though there have been many studies with different approaches, none of watermarking scheme is strongly enough to meet all requirements at the same time.
2. PROPOSED METHOD 2.1 Pre-Processing Phase The flowchart of pre-processing fingerprint image can be demonstrated in Figure with input is a fingerprint image and output is a high quality thinned fingerprint image.
Step 1) Filtering: Filtering will produce the high standard of the image. It make image clean and better contrast in the ridge and valley. There are several techniques to improve the standard of image from simple to complex and space domain to frequency domain.
Figure 1. Flowchart of pre-processing fingerprint
Step 2) Thresholding: It is the one of the methods of image segmentation. It is used to convert the gray level image into binary image. In this 8 bit grey scale image is converted into 1-bit image with 0 for ridges (black) and 1for valleys (white). This process is also known as image binarization. In this process we are taking the input image as filtered gray level image and applying thresholding on that image. There two types of thresholding techniques. They are global thresholding and local thresholding. In global thresholding we use the entire image to convert it into binary image. In local thresholding we divide image into number of block and applying thresholding technique to each and every block. Global thresholding is not much effective when compared with the local thresholding. There by we get the output image as binary image which is given in figure 4.
The execution of filters to the entire image is not much effective. Instead of applying filter to the entire image we can apply the
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Fig. 5 Thinning of the binary fingerprint image using morphological operations
Step 3) Thinning:
Figure 2. Filtering a fingerprint image using average and median filtering
Thinning is a morphological operation which is used to eliminate selected redundant pixels from binary images. In this process the ridges in the fingerprint image will be thinned by using such morphological operations. So, that we get the resultant image as thinned image. Thinning is used in many applications. It is usually used for skeletoning the image. This step will remove the fore-ground pixels in the ridges until the ridges are one pixel wide. So that we apply morphological operations [7] on the binary image which is resulted in image thresholding in step 2.
2.2 Embedding phase Firstly, the host image which is the used to hide the watermarked image is partitioned into 8×8 non-overlapping blocks [8]. The blocks which are partitioned are taken and apply DCT [4].
Figure 3. Filtering a fingerprint image using wiener filtering
DCT is one of the transformation techniques which is used to transform the block image into the transform coefficients without changing or effecting the actual size of the image. We apply DCT in the digital image which is in two dimensional forms. DCT provides more robustness compare to techniques used in the other spatial domain. This kind of algorithm provides more robustness against simple operation of image processing like brightness, blur of image. For transformation of a signal to frequency domain from spatial domain is done by DCT. For getting better performance/result it is used image compression. It is a kind of a function that provide technique used for image pixel in spatial domain to transform it frequency domain. In which we can eliminate redundancy. DCT is used frequency domain. In embedding process if the WM is applied in low frequency domain, the quality of the image will be reduced and if the WM is applied in high frequency domain, it suspects the attacks like filtering.
Figure 4. Gray scale image converted into Binary image
So, mid band DCT coefficients are used in watermark embedding process. The resultant image and the image to be hidden are embedded [3] and form a WM image [10] as shown in figure 6.
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the local ridge properties that emerge at a ridge ending or at ridge bifurcation. A ridge ending is produced out of the sudden ending of the ridge. Extraction of the fingerprint image is the reverse process of embedding method. In the extraction method, we are assuming that the watermarked image can be caused to attacks. In extracting phase the WM image is taken and extracts the image which is watermarked. There are several possible ways for the watermark extraction exists. Each of them depends on the parameters which owner has to extract watermark. The recovery image after applying extracting phase is shown in the figure 8.
Figure 6. Flowchart of embedding phase
Figure 8. Flowchart of extracting phase
Figure 7. Watermarked image after embedding
2.3 Extracting phase The fingerprint of any person cannot be changed and is unique. Fingerprint is produced by an impact of the order of the ridges exist in the finger. A single curved segment is known as a ridge and the space between the two adjoining ridges is called as a valley. Therefore, the uniqueness of the local ridge properties and their system of links are defined as a fingerprint. Minutiae points are
Figure 9. Recovery image from watermarked image after extracting
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2.4 Matching phase This phase is to authenticate the legal of image by matching with fingerprint database. We use distance calculation methods to find the accuracy in authentication. The Hamming distance between two vectors A = a1, a2….an and B = b1, b2…..bn can be calculated using (eq 1) as follows: --------(eq1)
[9] http://iris.usc.edu/VisionNotes/bibliography/contentschar.ht ml#OCR,%20Document%20Analysis%20and%20Character %20Recognition%20Systems [10] http://www.mathworks.in/help/images/discrete-cosinetransform.html [11] Malay Kishore Dutta, Anushikha Singh, K. M. Soni, Radim Burget, and Kamil Riha “Watermark Generation from Fingerprint Features for Digital Right Management Control”, 2013 IEEE.
If D is less than a threshold D0 (D0=0.5 by default) then 2 bit strings are matching. In these cases several matching vectors, the smallest D value is selected.
[12] Roli Bansal, Priti Sehgal, Veenu Bhasin, Punam Bedi, “Multi-Agent System for Intelligent Watermarking of Fingerprint Images” Dept. of Comp. Sc., University of Delhi, Delhi, India.
3. CONCLUSION
[13] T.Hoang Ngan Le, Kim Hung Nguyen, Hoai Bac Le, “A Robust Biometric Watermark-Based Authentication Scheme”, Department of Computer Science, University of Science, 227 Nguyen Van Cu, Ward 4, District 5, HCMC, Vietnam, 2010 IEEE.
In this research, fingerprint is used to authenticate the image by using watermarking technique. To make more robust we use DCT transformation in the watermarking process. So, that it give high security to the images which may not be accessible to the unauthorized parties. To effectively verify an image by fingerprint watermark, some techniques like filtering, thresholding and Image thinning method from fingerprint as well as improve accuracy of matching process. To make robust, the lowest frequency of DCT blocks is chosen to hold watermark. With this method, the authentication scheme can achieve high capacity and good quality.
4. REFERENCES [1] T.Hoang Ngan Le, Kim Hung Nguyen, Hoai Bac Le , “A Robust Biometric Watermark-Based Authentication Scheme” , 227 Nguyen Van Cu, Ward 4, District 5, HCMC, Vietnam, 2010 IEEE. [2] R. Gonzalez, R.Woods, “Digital Image Processing”, Prentice Hall, Englewood, Cliffs, NJ, 2002. [3] ] C. Ramos, R. R. Reyes, M. N. Miyatake, and H. P. Meana, “Image Authentication Scheme Based on Self-embedding Watermarking”, Lecture Notes In Computer Science, Vol. 5856, 2009, pp. 1005-1012. [4] ] N.X. Huy, T. Q. Dung, “An Image Watermarking Algorithm Using DCT Domain,” Journal on Information and Communication Technologies, Vietnam, 2002. [5] http://www.mathworks.in/help/images/removing-noise-fromimages.html [6] http://www.mathworks.in/help/images/ref/graythresh.html
[14] Zebbiche, Khalil, et al. "Protecting fingerprint data using watermarking."Adaptive Hardware and Systems, 2006. AHS 2006. First NASA/ESA Conference on. IEEE, 2006. [15] Sandhya Tarar, Ashish Kumar, Ela Kumar School of ICT,“A Fingerprint Watermarking Algorithm to Enhance The Privacy Of Fingerprint Data”, Gautam Buddha University, Greater Noida, India, Issue 8, Volume 9, August 2012, WSEAS Transactions on Information Science and Applications. [16] Bilge Gunsela; , Umut Uludaga, A. Murat Tekalpa, “Robust watermarking of fingerprint images”, B.Gunsel et al. Pattern Recognition 35 (2002) 2739 – 2747. [17] Yan Jia-ming, Liu liyuan,“An Information Hiding Algorithm Based on RDWT for Fingerprint Biometric System”, School of Electronics and Information Northwestern Polytechnical University XI’AN China, 2010 IEEE. [18] Jiang Yong Zheng Dong Hong Liang, Jiang Zeng Liang Miao Jin, “A DCT-BASED digital watermarking Algorithm FOR Image”, The 3rd Department of Artillery Command Academy Lang Fang, China, 2012 IEEE. [19] Fang Ma JianPing Zhang Wen Zhang, “A Blind Watermarking Technology Based on DCT Domain”, Institute of Information Engineering, Zhongzhou University, Zhengzhou, Henan, China 450044, 2012 International Conference on Computer Science and Service System
[7] http://www.mathworks.in/help/images/ref/bwmorph.html [8] http://www.mathworks.in/help/images/ref/blockproc.html
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