International Journal on Electronics & Wireless Communication Engineering (IJEWCE) ISSN: Volume 10 Issue 10 March 2018.

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1 MULTI-VISUAL CRYPTOGRAPHY FOR BIOMETRICS(FINGERPRINT & IRIS) USING LION OPTIMIZATION ALGORITHM Mr. S. Rajesh, Assistant Professor, Department of CSE, Avvaiyar College of Engineering & Technology for Women, Puducherry, India Ms. S. Shanmathi Priya, Department of CSE, Avvaiyar College of Engineering & Technology for Women, Puducherry, India Ms. Smera.P.P, Department of CSE, Avvaiyar College of Engineering & Technology for Women, Puducherry, India Ms. Nisha.D, Department of CSE, Avvaiyar College of Engineering & Technology for Women, Puducherry, India ABSTRACT An infrastructure build in the digital image and communication platform of biometric environment which is reliable to challenge the commercial and noncommercial IT development communities of data streams in high dimensional data cluster modeling. Biometrics are basically based on the expansion of pattern recognition systems. At present, electronic or optical sensors like cameras and scanning devices are used to capture images, recordings or measurements of a person s unique characteristics. These technologies are being utilized across a range of applications like security, prevention of cyber crime and border control, public aid/social benefits, customs, immigration, passport and healthcare identity verification, as well as commercial enterprises use. Most biometric systems that are typically use a single biometric trait to establish identity have some challenges like Noise in sensed data which increases False Acceptance Rate (FAR) of the system, Non-universality which reduces Genuine Acceptance Rate (GAR). Hence the security afforded by the biometric system mitigates its benefits. In this paper, we propose a Fused Multimodal systems which also have several advantages

2 over unibiometric systems such as, enhanced verification accuracy, larger feature space to accommodate more subjects and higher security against spoofing. The proposed enhanced multimodal authentication system is based on feature extraction(using fingerprint, retina and finger vein) and key generation (using RSA). Keywords: Multimodal, encryption key, ciphertext blocks, finger vein, LOA(Lion optimization algorithm). 1. INTRODUCTION The word "biometrics" is derived from the Greek words 'bios' and 'metric' which means life and measurement respectively. Biometrics means life measurement but the term is usually associated with the use of unique physiological characteristics to identify an individual. Biometrics is the automated recognition of individuals based on their behavioral and biological characteristics. Security is the application which most people associate with biometrics. However, biometric identification eventually has a much broader relevance as computer interface becomes more natural. Knowing the person with whom you are conversing is an important part of human interaction. The method of identification based on biometric characteristics is nowadays preferred over traditional passwords and PIN based methods for various reasons like the person to be identified is required to be physically present at the time-of-identification. Biometrics utilize something you are to authenticate identification. This might include fingerprints, retina pattern, iris, hand geometry, vein patterns, voice password or signature dynamics. Biometrics can be used with a smart card to authenticate the user. The use s biometric information is stored on a smart card, the card is placed in a reader and a biometric scanner reads the information to match it against that on the card. This is a fast, accurate and highly secure form of user authentication. Whether a human characteristic can be used for biometrics can be understood in terms of the following parameters: Universality indicates that each person should have the characteristic. Uniqueness means how well the biometric separates one individual from another. Permanence measures how well a

3 biometric resists aging and other variance over time. Collectability refers to ease of acquisition for measurement. Performance deals with accuracy, speed, and robustness of technology used. Acceptability is degree of approval of a technology. Circumvention is the ease of use of a substitute. A biometric system has two modes of operation viz. Verification A one to one comparison of a captured biometric with a stored template to verify that the individual is the one who she claims to be. Verification can be done in conjunction with a smart card, username or ID number (based on what she possesses or what she remembers ). Identification A one to many comparison of the captured biometric against a biometric database in an attempt to identify an unknown individual. The identification only succeeds in identifying the individual if the comparison of the biometric sample to a template in the database falls within a previously set threshold (based on who she is ). The method consists of mainly three stages: 1. Data Acquisition 2. Feature Extraction 3. Matching But this entire process is not that easy. There are few challenges that are faced by any biometric identification system 1. During identification the system has to operate on large dataset and the time taken by it to declare an identity should not be much. 2. To serve its purpose, an identification system needs an efficient searching and matching algorithm. 3. The number of false- positive in the system should not be very large as the size of the database increases. A biometric system is a pattern recognition system that works in the following way: acquires biometric data from an individual, extracts a feature set from the acquired data, and compares this feature set against the template set in the database. Biometrics commonly implemented or studied includes fingerprint, face, iris, voice, signature, and hand geometry. Many other modalities are in various stages of development and assessment. There is not one biometric modality that is best for all implementations. At present, electronic or

4 optical sensors like cameras and scanning devices are used to capture images, recordings or measurements of a person s unique characteristics. Biometrics are typically collected using a device called a sensor. These sensors serve two purposes: first they are used to acquire the data needed for recognition and then used to convert the data to a digital form. 2. EXISTING WORK In the Existing System, Biometrics identification has provided a robust technique for proving identity and has become the standard in identity authentication and access control. Iris recognition is a prudent biometric identification system. In our existing system RANSAC (Random Sample Consensus) is used for fitting ellipse around non circular iris boundaries. For iris localization they are detecting the inner boundary (boundary between iris and pupil) and the outer boundary (boundary between iris and sclera) of the input image. It was evaluated on WVU iris image database. The proposed segmentation algorithm begins by finding the darkest region in the iris image. Morphological operation such as dilation and erosion are applied to remove the above noise. Dilations fills up holes to remove the illumination inside the pupil region and erosions removes the spurious pixels. Which are dark spots caused by eyelashes. Dilation images is subtracted from the erosion image to obtain pupil boundary. This image is used as a mask for pupil localization. 3. PROPOSED WORK: Biometrics refers to metrics related to human characteristics. Biometric recognition can be used as a form of identification and access control. A biometric recognition system is used to identify individual in a group that are under surveillance. To provide authentication we have taken two physiological characteristics such as fingerprint and iris. We are going to provide authentication for our college website using biometrics. The system that we have proposed has addition security features when compared to the existing system. The first thing is that we are using two biometrics for the verification process. A single biometric itself provides security, but

5 sometimes they are prone to noise and are less efficient. So authentication for our college website will be provided only when the fingerprint and iris are matched. The fingerprint and iris are matched using lion optimization algorithm. In addition to that we are introducing visual cryptography for providing higher security. First the verification of user s fingerprint is done. This verification undergoes series of steps. First preprocessing such as noise removal then the contrast level of the fingerprint is improved using Histogram equalization. So that the fingerprint image is free from noise that make it easy for feature extraction. Then binarization is done which is followed by thinning image which is a morphological operation that is used to capture thin skins in the data. For fingerprint verification we are using minutiae point detection which is taken as a unique characteristic. Here the ridge bifurcation is identified where it captures the area that the ridge in the fingerprint divides into two parts. After the verification of fingerprint, the user s iris image will be verified. First the iris image is denoised using adaptive mean filtered image. Then histogram equalization is done to improve the contrast level of the iris image. Then the edges and boundaries are detected using entropy filtered image. For the verification of iris we are detecting the nodal boundaries which is unique for every person. Both the fingerprint and iris are matched using the Lion Optimization Algorithm which gives an optimal solution. Then we additionally introduce the concept of Visual Cryptography where the user needs to select the secret image. Visual cryptography is a cryptographic techniques which allows visual information to be encrypted in such a way that decryption becomes the job of the person to decrypt via sight reading. Only if the correct secret image is chosen, the authentication will be granted and our college website will be opened. If the secret image is not valid, then authentication will be denied. The visual cryptography has a homomorphic properties of cryptosystems where the pixel division / recognition is avoided and the encryption / decryption is performed on the cover pixels directly, so that the amount of encrypted data and the computational complexity are

6 lowered. In the lossless scheme, due to the probabilistic property, although the data of encrypted image are modified for data embedding, a direct decryption can still result in the original plain text image. OVERCOMES We are using two biometrics traits which is highly secured than a single biometrics. Lion Optimization algorithm is used to produce optimized results. Additional to this, visual cryptography is included to provide much higher security. Metrics such as Entropy, PSNR, SNR, SSIM are evaluated 4. EXPERIMENTAL DESIGNS AND RESULTS fig 1. flow diagram MODULES DESCRIPTION We are going to provide authentication for our college website in our proposed system. First the verification of user s fingerprint is done. This verification undergoes series of steps. After the verification of fingerprint, the user s it is image will be verifies. When both the fingerprint and iris is matched the secret image is to be chosen. Only if the correct secret image is chosen, the authentication will be granted and our college website will

7 be opened. If the secret image is not valid, then authentication will be denied. The following modules are used in this project 1. Verifying the Fingerprint 2. Verifying the Iris 3. Secret Image Contrast Image Contrast is the difference in luminance or color that makes an object distinguishable. In visual perception of the real world, contrast is determined by the difference in the color and brightness of the object and other objects within the same field of view. The human visual system is more sensitive to contrast than absolute luminance. Histogram equalization This method usually increases the global contrast of many images, especially when the usable data of the image is represented by close contrast values. Through this adjustment, the intensities can be better distributed on the histogram. This allows for areas of lower local contrast to gain a higher contrast. The method is useful in images with backgrounds and foregrounds that are both bright or both dark. Binarization A binary image is a digital image that has only two possible values for each pixel. Typically, the two colors used for a binary image are black and white. The color used for the object(s) in the image is the foreground color while the rest of the image is the background color. Thinning image Thinning is a morphological operation that is used to remove selected foreground pixels from binary images, somewhat like erosion or opening. It can be used for several applications, but is particularly useful for skeletonization. It will remove the selected foreground pixels from binary images. Thinning is normally only applied to binary images, and produces another binary image as output. It is used to eliminate the redundant pixels of ridges and also it captures thin skins in the data. The purpose of thinning stage is to gain the skeleton structure of fingerprint image.

8 Minutiae point detection The major minutia features of fingerprint ridges are ridge ending, bifurcation, and short ridge (or dot). The ridge ending is the point at which a ridge terminates. Bifurcations are points at which a single ridge splits into two ridges. Short ridges (or dots) are ridges which are significantly shorter than the average ridge length on the fingerprint. IMF points This is similar to the IMF graph. SECRET IMAGE In this module we are combining the fingerprint and iris module. We are using LION Optimization Algorithm for combining both fingerprint and iris. When both the fingerprint and iris are matched with each other then a secret image will be browsed from the database. Only when the secret image is correct, the authentication will be granted and the link of the college website will be opened IMF POINTS is stands for image fused points. In this point image is divided into pixelites. The pixelites are used to calculate the standard deviation based on maximum and minimum. Multi sensor Image fusion is the process of combining relevant information from two or more images into a single image. The resulting image will be more informative than any of the input images. Key image In this step the key image will be obtained. Extracted points 5.CONCLUSION AND FUTURE WORK In our project work we have designed a multi model biometrics (Fingerprint, Iris) for providing authentication which is highly secured. The prime focus is on feature extraction and optimization so as to achieve higher performance results than before. A realistic security analysis of the multi model biometric cryptography has also been conducted using Fingerprint and Iris. The overall performance of multi model system has increased with GAR by 95.3% and reduced with FAR of 0.01%, which is compared to unimodal biometric.

9 Future work can be further extended by accurately modeling feature extraction technique and by adding more number of fingerprint and iris images to the database and evaluating the matching methodology and it s performance of biometric system using different level of fusion. REFERENCES [1] A. Akavia, S. Goldwasser, and V. Vaikuntanathan, Simultaneous hardcore bits and cryptography against memory attacks, in Proc. 6th Theory Cryptography Conf., 2009, pp [2] S. S. Al-Riyami and K. G. Paterson, Certificateless public key cryptography, in Proc. 9th Int. Conf. Theory Appl. Cryptol., 2003, pp [3] M. H. Au, J. K. Liu, W. Susilo, and T. H. Yuen, Certificate based (linkable) ring signature, in Proc. Inf. Security Practice Experience Conf., 2007, pp [4] M. H. Au, Y. Mu, J. Chen, D. S. Wong, J. K. Liu, and G. Yang, Malicious KGC attacks in certificateless cryptography, in Proc. 2nd ACM Symp. Inf., Comput. Commun. Security, 2007, pp [5] M. Blaze, G. Bleumer, and M. Strauss, Divertible protocols and atomic proxy cryptography, in Proc. Int. Conf. Theory Appl. Cryptographic Techn., 1998, pp [6] A. Boldyreva, V. Goyal, and V. Kumar, Identity-based encryption with efficient revocation, in Proc. ACM Conf. Comput. Commun. Security, 2008, pp [7] D. Boneh, X. Ding, and G. Tsudik, Fine-grained control of security capabilities, ACM Trans. Internet Techn., vol. 4, no. 1, pp , [8] D. Boneh and M. Franklin, Identitybased encryption from the Weil pairing, in Proc. 21st Annu. Int. Cryptol. Conf., 2001, pp [9] R. Canetti and S. Hohenberger, Chosenciphertext secure proxy re-encryption, in Proc. ACM Conf. Comput. Commun. Security, 2007, pp [10] H. C. H. Chen, Y. Hu, P. P. C. Lee, and Y. Tang, NCCloud: A network- codingbased storage system in a cloud-of-clouds, IEEE Trans. Comput., vol. 63, no. 1, pp , Jan [11] S. S. M. Chow, C. Boyd, and J. M. G. Nieto, Security-mediated certificateless cryptography, in Proc. 9th Int. Conf.

10 Theory Practice Public-Key Cryptography, 2006, pp