IRJET- Digital Image Processing based Vote Polling using Mat Lab and Cryptography
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The document discusses a proposed digital image processing and cryptography based voting system using fingerprint recognition. The system would use fingerprint scanners to create a voter database, and MATLAB for image processing. On voting day, voters' fingerprints would be scanned and matched to the database to authenticate them and prevent fraud. It describes how the system could identify voters' age, gender, and name from their fingerprints. The aim is to secure the voting process and address issues like theft and forgery.
![International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 05 Issue: 03 | Mar-2018 www.irjet.net p-ISSN: 2395-0072
© 2018, IRJET | Impact Factor value: 6.171 | ISO 9001:2008 Certified Journal | Page 1771
REFERENCE
[1] User's Guide to NIST Biometric Image Software (NBIS)
[2] User's Guide to NIST Biometric Image Software Export
Control(NBIS-EC)**
[3]Cygwin library and associated tools (www.cygwin.com).
[4] E. A. Bretz, “Slow Takeoff”, IEEE Spectrum, September,
pp. 37-38, 2002.
[5] E. A. Bretz, “Delayed Arrival for U.S. Baggage Screening?”,
IEEE Spectrum, May, pp. 16-19, 2002.
[6] InVision Technologies, www.invision-tech.com, USA.
[7] G. C. Giakos, N. Shah, S. Chowdhury, “A novelvsensor for
X-ray imaging applications”, IEEE
[8] Transactions on Instrumentation and Measurement,Vol.
49, pp. 300 –306, 2000.
[9] T. W. Wang, J. P .O. Evans, “Stereoscopic dual energy X-
ray imaging for target materials identification”, IEE
Proceedings of Vision Image and Signal Processing, Vol.150,
pp. 122 –130,2003
[10] S. Singh and M. Singh, “Review: Explosives detection
systems (EDS) for aviation security”, Signal Processing,
Elsevier, Vol. 83, 2003.
BIOGRAPHIES
Sivanesan Rajangam, Assistant
Professor, Department of Computer
Applications. Have Five Years of
Teaching Experience in Esteemed
Institutions and Corporate
Experience as well.
Research Areas: Data Mining, Image
Processing and Compiler Design.](https://image.slidesharecdn.com/irjet-v5i3396-190123072528/85/IRJET-Digital-Image-Processing-based-Vote-Polling-using-Mat-Lab-and-Cryptography-4-320.jpg)
IRJET- Digital Image Processing based Vote Polling using Mat Lab and Cryptography
- 1. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 05 Issue: 03 | Mar-2018 www.irjet.net p-ISSN: 2395-0072 © 2018, IRJET | Impact Factor value: 6.171 | ISO 9001:2008 Certified Journal | Page 1768 DIGITAL IMAGE PROCESSING BASED VOTE POLLING USING MAT LAB AND CRYPTOGRAPHY Sivanesan.R1, Manikandan.G2,Ashwin.S3, Vignesh.P4 1Assistant Professor, Department of BCA & M.Sc SS, Sri Krishna Arts and Science College, Coimbatore, Tamilnadu, India. 2,3,4 Department of BCA & M.Sc SS, Sri Krishna Arts and Science College, Coimbatore, Tamilnadu, India. ---------------------------------------------------------------------***--------------------------------------------------------------------- ABSTRACT - In this modern society, theft has become major concern for protecting the information. The loss of valuable information leads to a number of personal and legal issues. People are concerned about securing access.Duetoincreasein theft & forgery in polling each and every human losing their fundamental rights to poll vote for their leader. As a result of this, the democracy has been shifting more towardsautocracy. As a responsible people, we should present a way to fraud detection using this “Fingerprint Technology”. Keywords: Image Processing, Mat Lab, Cryptography, Finger Print, Security. 1. INTRODUCTION 1.1 IMAGE PROCESSING We use the sensors to scan the fingerprint image, and we create a database of scanned images which are stored in a network in a distributed manner. 1.2 MATLAB We use a AI techniques and mat lab program to process the image. 1.3 NETWORK Image database is stored in a distributed manner and each district & state. We can protect the data using the algorithm which we developed “SHIFT DIFFERENCEALGORITHM”.We use end – end encryption: ie encryption and decryption is done only at receiver and sender side. Using sensors, we are able to provide information about the age, gender, andname of the voter. Whenever voters register his vote, his fingerprint is searched in database and checks whether the voter has already voted or not. This avoids fraud, this is our primary focus. 1.4 BIOMETRICS Biometrics enables the identification of a person based on his or her physical characteristicsand/orbehavior.Common biometrics include: fingerprint, voice pattern,retinalpattern and facial features. Among this large variety of biometric possibilities, the use of fingerprint for identification and verification dominates the market. There are many reasons for this including the low cost, high-reliability and fast response of the fingerprint technology and systems. Theuse of fingerprints for user authentication hasbeenontheriseas people have discovered many problems with password and hardware token-based systems. Fig. 1.1 Finger Print Scanning 2. DIGITAL IMAGE PROCESSING Digital image processing methods were introduced in 1920, when people were interested in transmitting picture information from one image of size 256*256 was about a week. Steps in digital image processing are as follows, 1. Image grabbing or acquisition 2. Preprocessing 3. Segmentation 4. Representation and feature extraction 5. Recognition and interpretation The main aspects in digital image processing are image representation. Any monochrome image can representedby means of a two-dimensional light intensity function f(x,y), where x and y denotesspatial co-ordinatesand thevalueofx at any point(x,y) is the gray level or the brightness of the image at the point. 2.1 MATLAB MATLAB is known as matrix laboratory which is used for coding the programs for analyzing and predicting the age, gender and identification of human. At first we convert the rgb image into the gray scale image. Normally the image processing tool box is used to read the image and show the image. It has two advanced image processing concepts.They are
- 2. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 05 Issue: 03 | Mar-2018 www.irjet.net p-ISSN: 2395-0072 © 2018, IRJET | Impact Factor value: 6.171 | ISO 9001:2008 Certified Journal | Page 1769 1. Read and display an image 2. Adjust the contrast The edge calculation is the important of image identification process thus the program is given below: Steps involved in collecting DATABASE: ENROLLMENT: 1. Fingerprint scanning 2. Image capture 3. Minutiae extraction 4. Save template in memory VERIFICATION: 1. Fingerprint scanning 2. Image capture 3. Minutiae extraction 4. Verification template 5. ACCEPT or REJECT 2.2 GENDER IDENTIFICATION A novel method for human gender classification by measuring the Raman spectrum of fingernail clippings. As Raman spectroscopy revealsthe characteristics of vibration frequencies of the fingernails, it provides unique chemical fingerprints that can be used to describe the molecular structure differences of fingernail between males and females. Fig 2.1 Verification and Enrollment Asthe differences of Raman spectra of human fingernailsare very subtle, they are enhanced by usingapatternrecognition method. In the present study, a combination algorithm of principal component analysis (PCA) and support vector machines (SVM) was implemented to perform the data classification. This combined algorithm provides a classification accuracy of up to 90%. The success of this present method may be used as an alternative rapid tool to identify human genderin forensic applications. Fig 2.2 Gender Identification 2.3 AGE IDENTIFICATION Similar to our gender identification the minutiae s using to predict the cellslife with the help of genetic algorithminmat lab & C language 2.4 PICTURIZATION Fig 2.4 Picturization
- 3. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 05 Issue: 03 | Mar-2018 www.irjet.net p-ISSN: 2395-0072 © 2018, IRJET | Impact Factor value: 6.171 | ISO 9001:2008 Certified Journal | Page 1770 EXPERIMENT Fig. 2.5 Technical Evaluation of Fingerprint Scanner Fig 2.6 Fingerprint Networking 2.5 SECURE NETWORK VERIFICATION In network we use distributed network for every state as well as every district so time should minimizing automatically verify the database which we saved previously. The flow chart explain very simple manner. FLOW CHART Fig .2.7 Biometric Reader Process REGISTRATION STAGE and VERIFICATION STAGE are two discrete stages. BIOMETRIC READER: It accepts a user’s analog fingerprint and transforms it into digital information. PROCESSING UNIT: it takes input as the raw information provided by the reader, and extracts the onion layers from the data. These are send to meta-processing unit (during registration) or to the comparison unit (during verification). META PROCESSING UNIT: It isolates the smallest convex polygon from any set of onion layers it get from the processing unit and submits the reference database. COMPARISON UNIT: It intersects and compares the onion layers provided by the processing unit with the reference polygon provided by the reference database. REFERENCE DATABASE: It stores the user’s reference polygons, or provided by the meta-processing unit during registration or provides a user’s reference polygon. 3. CONCLUSION Taking into account of the results of the experiment uses to deliver election result very quickly, and also explainwhoare all supporting (gents, women’s, youngsters, old age people)like this we segregate our results within one hour.
- 4. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 05 Issue: 03 | Mar-2018 www.irjet.net p-ISSN: 2395-0072 © 2018, IRJET | Impact Factor value: 6.171 | ISO 9001:2008 Certified Journal | Page 1771 REFERENCE [1] User's Guide to NIST Biometric Image Software (NBIS) [2] User's Guide to NIST Biometric Image Software Export Control(NBIS-EC)** [3]Cygwin library and associated tools (www.cygwin.com). [4] E. A. Bretz, “Slow Takeoff”, IEEE Spectrum, September, pp. 37-38, 2002. [5] E. A. Bretz, “Delayed Arrival for U.S. Baggage Screening?”, IEEE Spectrum, May, pp. 16-19, 2002. [6] InVision Technologies, www.invision-tech.com, USA. [7] G. C. Giakos, N. Shah, S. Chowdhury, “A novelvsensor for X-ray imaging applications”, IEEE [8] Transactions on Instrumentation and Measurement,Vol. 49, pp. 300 –306, 2000. [9] T. W. Wang, J. P .O. Evans, “Stereoscopic dual energy X- ray imaging for target materials identification”, IEE Proceedings of Vision Image and Signal Processing, Vol.150, pp. 122 –130,2003 [10] S. Singh and M. Singh, “Review: Explosives detection systems (EDS) for aviation security”, Signal Processing, Elsevier, Vol. 83, 2003. BIOGRAPHIES Sivanesan Rajangam, Assistant Professor, Department of Computer Applications. Have Five Years of Teaching Experience in Esteemed Institutions and Corporate Experience as well. Research Areas: Data Mining, Image Processing and Compiler Design.