SECURED TRANSMISSION OF BIOMETRIC CONTENT USING VISUAL CRYPTOGRAPHY

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1 Volume 119 No , ISSN: (on-line version) url: SECURED TRANSMISSION OF BIOMETRIC CONTENT USING VISUAL CRYPTOGRAPHY S. EsaiPuvanesh UG Scholar, CSE Vel Tech Multi Tech Dr,Rangarajan Dr.Sakunthala Engineering College, S. Prakasam UG Scholar, CSE Vel Tech Multi Tech Dr,Rangarajan Dr.Sakunthala Engineering College S. Vignesh UG Scholar, CSE Vel Tech Multi Tech Dr,RangarajanDr.Sakunthala Engineering College G. Madasamy Raja, Associate Professor, Department of Computer Science and Engineering Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Chennai India ABSTRACT - Biometric technology is the safest technology to implement the security measures to measure a person s unique physical and behavioral characteristics. The main aim of the biometric technology is to ensure that the authenticated person is using the system and thereby avoiding the unauthorized access. The idea behind the biometric technology is the concept that every human being can be correctly identified by his or her inherent physical or behavioral traits. The physical traits may be in the form of fingerprint recognition, retina recognition, facial recognition, iris recognition, voice recognition, etc. In the supervised manner, initially biometric features of a particular person is extracted and stored in the biometric features database and the system is trained with those features. Later that particular person is undergoing for 1557

2 the biometric checking to verify his or her authentication. Nowadays the security of those biometric features are very very important in the sense, if those features are stolen or accessed by the hackers, then the hackers can simulate a person with those biometric features for the fraudulent activities. The main aim of this study is related to security and privacy of biometric content, that is the biometric features are encrypted through edge computing that results in improved security and privacy of biometric and other critically private information. KEYWORDS: Biometric Technology, Visual Cryptography, Discrete Wavelet Transform, Encryption, Decryption. 1. INTRODUCTION The problems related to security and privacy of biometric content is a major concern in providing security to critically private information. Visual cryptography is an approach to secure data that is to be shared through generating multiple shares. There are numerous cases of individual privacy breaches relating to personal information and multimedia content due to the limited protection. The proposed work deals with the security of biometric content, specially face images. Fig. 1 shows the block diagram for the fundamental image processing tasks. The sub task preprocessing is one of the important tasks in any kind of image processing application. The protection mechanism in the preprocessing should not degrade the visual quality of the image so that the difference of the recognition rate before and after the application of the mechanism should be a minimum. The key function of preprocessing is to improve the image in ways-that increase the chances for success 1558

3 of the other processes. Preprocessing typically deals with techniques for enhancing contrast, removing noise, and isolating regions whose texture indicate a likelihood of alphanumeric information. This study selects wavelet transform for preprocessing before the biometric features are extracted. Fig. 2 shows the architecture diagram for the encryption and decryption process. The organization of this paper is as follows: section 2 deals with the related works relevant to present visual cryptography technique for hiding the biometric images. Section 3 describes about the proposed algorithm with block diagram and shows some results. Section 4 throws some light on the future extension of this study and finally sections 5 conclude this paper. Fig. 1 Fundamental steps in digital image processing 1559

4 Input The face image is captured from the user. Conversion The RGB color image is converted to grayscale. Preprocessing The image is decomposed using DWT. Encryption The preprocessed image is encrypted and stored in database. Decryption When user wants to access the files the sample image is decrypted. Biometric matching The captured image and sample image are verified. Authorization In case of face recognition, the faces are matched for a successful match, else error message will be displayed. 2. RELATED WORK In the existing method, the encryption mechanism was used with watermarking [1-4], to protect biometric templates. This technique was achieved through a 2-share mechanism on the biometric template and watermarking is done by swapping the Discrete Cosine Transform (DCT) coefficients. The major disadvantage in this method is the lack of privacy. There is a high risk, where the biometric templates might be tampered or 1560

5 misused during the transmission. Moreover, nowadays we are seeing a rapid development in the field of IoT which almost sneaks into the each and every major life applications of people and it is also expected that IoT and Its applications are going to dominate in the Information 3. PROPOSED SYSTEM The proposed system for the security of biometric images safety saving is enabled through the Weber Local Descriptor (WLD) based zerowatermarking allows for watermarking the unique user identity number into the image. Weber Local Descriptor (WLD) uses Weber Discrete Wavelet Transform for feature extraction and creating the shares. Zero-watermarking allows for the water marking of multimedia content but without making any changes to it. This is so called watermarked image using visual cryptography [7, 8]. Visual cryptography technology field in the near future [5, 6]. Under this scenario, this kind of security thread is really dangerous. So, this study focuses the safety storage of the biometric features for safer transmission or some important database like adhaar card database, bank database etc. (VC) method changes the input image into many numbers of alternate images which are called shares. When we regroup the shares, we can recover he input image back. These shares individually reveal no information about the contents of the image and even if an eavesdropper or attacker is able to access to the images, he/she will not able to collect meaningful information by decrypting the image. The following are some important benefits that we are going to get form this proposed system: This method gives us a better chance in preserving the integrity of the biometric content. 1561

6 Since we have decomposed the image, even if a hacker hacks the transmission and gets the image it would be impossible to decrypt the image. This increases the privacy of the biometric content and improves the security Encryption and Decryption Mechanism After the completion of the preprocessing stage, the image comes to the encryption process. Here the image features [9] are identified from the processed image. The image is then subjected to visual cryptography, which generates the shares or visually encrypts the image. After the shares are generated they are shared on the cloud where further processing for different applications (e.g. face authentication, based on the same secret key used in the encryption process) takes place. The entities that have legal access to the images can access accordingly. The encrypted image is stored on the cloud database. The visual decryption procedure is then applied to generate the received zero-watermarked image. This is the reverse of the DWT process. The user has to give an input image. The system gets the input image and retrieves the sample images from the database. The decryption process is applied to the sample images and it is verified with the input image. The received image is then decomposed using the Discrete Wavelet Transform up to level 2. The image verification is done through Viola Jones face detection algorithm. Image matching and image recognition is done to the images to compare the authenticity of the images. If the image is verified the user can access the files, otherwise the user will not be authorized to view the file. 1562

7 4. FUTURE ENHANCEMENTS The scope of our study is limited only to the face recognition biometric system, which can be further extended to combine other biometric templates also with face template. If so, it will be considered as multimodal biometric visual cryptography. 5. CONCLUSION We have chosen the Weber Discrete Wavelet Transformation and used it to preserve the privacy of the biometric contents like finger print template, iris templates, facial images that are used for enhancing the security features. We have used a face recognition security method which can be used to increase the security of files stored on the cloud database. We have increased the privacy of the transmission channel. Our algorithm is mainly used to preserve the integrity of the facial images, thus using technology to provide a better security mechanism. 5. REFERENCES [1] G. Badshah, S. C. Liew, J. M. Zain, and M. Ali, ``Watermark compression in medical image watermarking using Lempel-Ziv-Welch (LZW) lossless compression technique,'' Journal of Digital Imaging, vol. 29, no. 2, pp , Apr [2] O. Nafea, S. Ghouzali, W. Abdul, and E. U. H. Qazi, ``Hybrid multi bio-metric template protection using watermarking,'' The Computer Journal, vol. 59, no. 9, pp , Sep [3] S. Ghouzali, ``Watermarking based multi-biometric fusion approach,'' in the Proceedings of First International Conference on Codes, Cryptology and Information security, Rabat, Morocco, pp , May [4] M. A. M. Abdullah, S. S. Dlay, W. L. Woo, and J. A. Chambers, A frame work for iris biometrics protection: A marriage between watermarking and visual cryptography,'' IEEE Access, vol. 4, pp , Nov

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