voting ATM by gopishrine

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Abstract
Riots in election have become a common scenario now days. Even though the Election commission has taken enough steps to control riots they are unable to so. Even though the photo identity cards are introduced it hasn’t solved the problem on the whole. the optimum security is lagging. So forced voting is still taking place.

This paper gives an idea to obtain privacy and high security using ATM as a voting machine. A separate real time operating system is installed with the help of a 24hr video coverage and fingerprint application for security. This eliminates even the disadvantages of Electronic Voting Machine and gives the freedom for public to vote as per their wish.

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Table of contents

Introduction to fingerprints Veri Finger Algorithm Definitions ATM Fundamentals of RTOS Working of ATM voting machine Disadvantages of earlier Voting machine Advantages of Voting ATM Conclusion

3 4 5 6 7 9 10 10 11 11

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Introduction to Fingerprints
Human fingerprints are unique to each person and can be regarded as a sort of signature, certifying the person's identity. The most famous application of this kind is in criminology. However, nowadays, automatic fingerprint matching is becoming increasingly popular in systems which control access to physical locations, computer/network resources, bank accounts, or register employee attendance time in enterprises. Straightforward matching of the to-be-identified fingerprint pattern against many already known patterns would not serve well, due to the high sensitivity to errors (e.g. various noises, damaged fingerprint areas, or the finger being placed in different areas of fingerprint scanner window and with different orientation angles, finger deformation during the scanning procedure). A more advanced solution to this problem is to extract features of so called minutiae points (points where capillary lines have branches or ends) from the fingerprint image, and check matching between the sets of fingerprint features. However, the above outlined solution requires sophisticated algorithms for reliable processing of the fingerprint image, noise elimination, minutiae extraction, rotation and translation-tolerant fingerprint matching. At the same time, the algorithms must be as fast as possible for comfortable use in applications with a large number of users. For developers who intend to implement the fingerprint recognition algorithm into a microchip, compactness of algorithm and small size of required memory may also be important. Though many fingerprint identification algorithms have been proposed, in reality, achieving satisfactory fulfillment of all the discrepant requirements is still an important problem. Compared to larger, PC-based systems, the fingerprint recognition algorithm for embedded systems requires some specific features and different commonly accepted requirements for things like reliability and speed. Embedded devices usually have weaker processors than personal computers. The fingerprint image processing routines (image enhancement, noise filtration, binarization, skeletonization etc.), used for PC-based applications are quite computationally expensive, therefore require substantial algorithm modification to achieve acceptable image processing time (1 second or less) on the embedded device

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VeriFinger
In 1998 Neurotechnologija developed VeriFinger, a fingerprint identification algorithm, designed for biometric system integrators. Since that time, Neurotechnologija has released 10 algorithm versions, with the current version, VeriFinger 5.0, providing the the most powerful fingerprint recognition algorithms to date: Reliability. Even earlier VeriFinger fingerprint identification algorithm versions consistently have shown some of the best results for reliability in several biometric competitions, including the International Fingerprint Verification Competition (FVC2004, FVC2002 and FVC2000) and the National Institute of Standards & Technology (NIST) Fingerprint Vendor Technology Evaluation (FpVTE 2003), where Neurotechnologija ranked among the top five companies for accuracy in single-finger tests. VeriFinger 5.0 provides major reliability improvements over these earlier versions. Fingerprint matching speed is one of the highest among the competing identification algorithms. Fingerprint enrollment time is 0.2-0.4 sec., and VeriFinger can match 40,000 fingerprints per second in 1:N identification mode. To confirm these results with your data, please try VeriFinger algorithm demo. VeriFinger algorithm includes image quality determination and features generalization which can be used during fingerprint enrollment to ensure that only the best quality fingerprint template will be stored into database. VeriFinger is offered for a competitive price. Developers can select from several types of SDK and licensing models. Each of these kits and models is intended for specific needs, and developers always can make an upgrade by paying the difference between the current and more powerful SDK.

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Algorithm
The FingerCell algorithm is similar to the VeriFinger algorithm and includes these features: FingerCell is fully tolerant to fingerprint translation, rotation and deformation. Such tolerance is achieved by our proprietary fingerprint matching algorithm. FingerCell does not require the presence of fingerprint core or delta points in the image and can recognize a fingerprint from any part of it. FingerCell has fingerprint enrollment with features generalization mode. This mode generates a collection of the generalized fingerprint features from a collection of fingerprints of the same finger. Each fingerprint image is processed and features are extracted. Then the collection of features is analyzed and combined into a single generalized features collection which is written to the database. This way, enrolled minutiae are more reliable and the fingerprint recognition quality considerably increases using this enrollment mode. FingerCell can use database entries which were pre-sorted using certain global features. Fingerprint matching is performed first with the database entries having global features most similar to those of the test fingerprint. If matching within this group yields no positive result, then the next record with most similar global features is selected, and so on until the matching is successful or the end of the database is reached. In most cases there is a good chance that the correct match will be found at the beginning of the search. As a result, the number of comparisons required to achieve fingerprint identification decreases drastically, and correspondingly, the effective matching speed increases. The FingerCell embedded algorithm is similar to VeriFinger, but it has about 4 times faster image processing and feature extraction algorithms

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Definitions
1. The basis for fingerprint identification are two axioms: Fingerprints are unique Fingerprints do not change during life. 2. The basis of fingerprint identification practices is the fact that the unique nature of fingerprints is expressed in papillary ridges that show features of a principal nature that keep their properties even under adverse conditions. Location, direction and relations stay the same when printed under pressure, while stretching the flexible skin, and even with distortion, to a relative high level. 3. Scientific research and extensive practice have shown that fingerprints, after development in the womb, do not change during life (and even until long after death), preserve ridge patterns and detail. It has been proved after long research and years of practice that the principal aspects from the ridge detail do not change with growth. As the detail is embedded in the dermis or true skin, it is restored to the original when the skin comes to rest after temporary damage to the epidermis or outer skin such as burns, blisters, abrasion and even callus. Only when the dermis is affected after external damage e.g. with a deep wound, the skin will develop scar tissue changing the papillary detail. This detail becomes permanent, however, after some time and may make that piece of skin even more distinctive. 4. Identification is: 'the conclusion of an expert that two fingerprints show sufficient information in agreement, and no principal differences, in order to point one donor as the sole source, and whose conclusion is verified and confirmed by another independent expert'. 5. Identifications require sufficient coinciding information between two prints, if features are present in one print and absent in the other and there is no rational explanation based on findings and facts. A statement of identification should not be given in principle. 6. Features can be described as minutiae and other ridge formations. The minutiae is an event which occurs in a regular flow of papillary ridges. The event is a natural/ biological disturbance to the normal parallel system of the ridges (e.g. a ridge stops or starts). 7. The value of the event is given by the rarity of the occasion taking into account the type of direction, relations to other points and the position in the pattern. The quality value is related to clarity and the presence of ridge detail. Two or more points that coincide/overlap count for one point/event only. [For example two or three lines that come from different directions which join at the delta point.]. Page 7 of 12

8. Other ridge formations relate to the shape, position and orientation of pores and the shape and configuration of individual ridges (the study known as ridgeology). 9. A point of agreement is a point in compared prints where location and appearance have a similarity that meets a specific value and where that similarity falls within the ruling tolerance. 10. Look-alikes are fingerprints from different origin that show an unexpected level of similarity that has the potential danger of a false conclusion about identity.

ATM
Short for Asynchronous Transfer Mode, a network technology based on transferring data in cells or packets of a fixed size. The cell used with ATM is relatively small compared to units used with older technologies. The small, constant cell size allows ATM equipment to transmit video, audio, and computer data over the same network, and assure that no single type of data hogs the line. Some people think that ATM holds the answer to the Internet bandwidth problem, but others are skeptical. ATM creates a fixed channel, or route, between two points whenever data transfer begins. This differs from TCP/IP, in which messages are divided into packets and each packet can take a different route from source to destination. This difference makes it easier to track and bill data usage across an ATM network, but it makes it less adaptable to sudden surges in network traffic.

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Figure 2: The ATM block diagram

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Fundamentals of RTOS
To most people, embedded systems are not recognizable as computers. Instead, they are hidden inside everyday objects that surround us and help us in our lives. Embedded systems typically do not interface with the outside world through familiar personal computer interface devices such as a mouse, keyboard and graphic user interface. Instead, they interface with the outside world through unusual interfaces such as sensors, actuators and specialized communication links. Real time and embedded systems operate in constrained environments in which computer memory and processing power are limited. They often need to provide their services within strict time deadlines to their users and to the surrounding world. It is these memory, speed and timing constraints that dictate the use of real-time operating systems in embedded software Basic kernel services

Figure 1: an RTOS Kernel provides an Abstraction Layer between Application Software and Embedded Hardware In the discussion below, we will focus on the "kernel" ? the part of an operating system that provides the most basic services to application software running on a processor. The "kernel" of a real-time operating system ("RTOS") provides an "abstraction layer" that hides from application software the hardware details of the processor (or set of processors) upon which the application software will run.

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Working of ATM voting machine:
Each voter to vote in the ATM is given a smart card. Insert the smart card into the machine. The details of the voter will be displayed. To confirm the details, the voter has to provide the system with his fingerprint with the help of the device available.

Then the voter form will be displayed on the screen. Using the touch screen he/she has to vote by selecting her nominee (only selected and not voted). To confirm voting the voter has to provide his fingerprint once again. Immediately his vote will be registered in the main server placed in the election commission office. A receipt will be printed out.

Disadvantages of earlier Voting machine:
Transport problems before and after elections. Cost of setting up booths with high security (man power). Misuse of EVM can be avoided.

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Advantages of Voting ATM
Very high security. No need for counting. No riots. Vote can be registered any where across the country. No black votes, highly personalized. Free gifts. Unregistered votes can not be misused.

Conclusion
This voting machine removes the present disadvantages of voting system and gives a very high security. This improves the voting percentage as today’s one of the main drawback is people feel lazy to vote and their votes are misused by others.

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