This paper explores biometrics and electronic voting in a bid to design a Biometrics-based E-Voting System that could be applicable in Ghana and other similar developing countries, its shortcomings and the advantages of the biometrics authentication module incorporated in the system.

1. IEEE PENTECOST UNIVERSITY COLLEGE STUDENT BRANCH
2010
Using Biometrics to address Voter Authentication
IEEE Ghana Section Student Paper Contest Entrant
Obafemi Adedamola Akin-Laguda

2. Introduction
Technology has been used to advance and improve efficiency in various human endeavours and it goes without saying that an electronic system for casting and counting votes is not only essential but inevitable. But an important part of the voting process that has always caused a lot of problems for electoral commissions has been the authentication of genuine voters to legitimize the election result. One way of ensuring this to the authentication of voters based on extremely unique characteristics and that brings biometrics into the equation.
The biometric concept has been in use since time immemorial in the identification of people. Think to the way you recognize a friend; by their build from a distance, their face from a closer proximity, the sound of their voice over a telephone line or by their behaviour in a reported situation. Non-electronic biometrics is already a part of our daily life.
This paper explores biometrics and electronic voting in a bid to design a Biometrics-based E-Voting System that could be applicable in Ghana and other similar developing countries, its shortcomings and the advantages of the biometrics authentication module incorporated in the system.
Biometrics
Biometrics is used to refer to the science of identifying human beings based on the measurement and analysis of inherent biological features. And with regards to information technology it is used as a secure method of identification for access control mechanisms.
A Biometrics system is basically a pattern recognition system that compares a feature, either physiological of behavioural, that a person possesses to a predefined template of the same feature. In the 1890s, anthropologist Alphonse Bertillion developed the “Bertillonage” (a method of body measurement) in a bid to stop repeat offenders from using aliases, hair changes, weight loss or gain to create new identities. The problem that led to the decline of the Bertillonage is that the characteristics used in enrolling the criminals were not necessarily unique to all people e.g. size of their skull or length of their fingers. And this brings us to what characteristics can be used in a biometrics system.
There are criteria that a potential biometric feature must fulfil before it can be used in a biometric system. These criteria would help the system evade the problems experienced by the Bertillonage.
The criteria are;
1. Universality: The feature should be possess by everyone
2. Distinctiveness: The feature should be unique
3. Permanence: The feature should be invariant over a long period of time
4. Collectability: The feature should be acquirable but also harmless to obtain from the user
5. Circumvention: The feature must be robust enough to handle various fraudulent methods
Based on the criteria listed above, not all features can be effectively used in a biometric system. The features listed below are commonly used in a biometric system as they fulfil to some degree the criteria above and have been arranged according to type.
Physiological Features
DNA – Deoxyribonucleic acid (DNA) is probably the most reliable biometrics. It is in fact a one-dimensional code unique for each person. Exceptions are identical twins.
Shortcomings:
I. contamination and sensitivity, since it is easy to steal a piece of DNA from an individual and use it for an ulterior purpose,
II. no real-time application is possible because DNA matching requires complex chemical methods involving expert's skills,
III. Privacy issues since DNA sample taken from an individual is likely to show susceptibility of a person to some diseases. All this limits the use of DNA matching to forensic applications.
Infrared thermogram (facial, hand or hand vein) – It is possible to capture the pattern of heat radiated by the human body with an infrared camera. These patterns are thought to be unique and acquired through a non-invasive method, but image acquisition is rather difficult due other heat emanating surfaces near the body. This technology could be used for covert recognition as authentication can be done from a distance. A related technology using near infrared imaging is used to scan the back of a fist to determine hand vein structure, also believed to be unique.
Shortcomings:
I. Like face recognition, it must deal with the extra issues of three-dimensional space and orientation of the hand i.e. the face and hands look differently when view from different angles
II. Another potential problem is that infrared sensors are expensive.

3. Face – Facial images are the most common biometric characteristic used by humans to make a personal recognition, hence the idea to use this biometric in technology. This is a nonintrusive method and is suitable for covert recognition applications. Face verification involves extracting a feature set from a two-dimensional image of the user's face and matching it with the template stored in a database.
Shortcomings:
I. Difficulties in recognizing a face from images captured from two different angles and under different ambient illumination conditions
II. Another problem is the fact that the face is a changeable social organ displaying a variety of expressions.
Retina – Retinal recognition creates an "eye signature" from the vascular configuration of the retina which is supposed to be a characteristic of each individual and each eye, respectively. Since it is protected within the eye itself, and since it is not easy to change or replicate the retinal vasculature, this is one of the most secure biometric. Image acquisition requires a person to look through a lens at an alignment target; therefore it implies cooperation of the subject. Also retinal scan can reveal some medical conditions and as such public acceptance is questionable.
Shortcomings:
Medical information privacy issues
Iris – The Iris is the coloured ring around the pupil of every human being and like a snowflake, no two are alike. Every Iris exhibits a distinctive pattern that is formed randomly in utero, in a process called chaotic morphogenesis. Iris scanning is less intrusive than retinal because the iris is easily visible from several meters away. Responses of the iris to changes in light can provide an important secondary verification that the iris presented belongs to a live subject.
Shortcomings:
While the iris seems to be consistent throughout adulthood, it varies somewhat up to adolescence.
Ear – It has been suggested that the shape of the ear and the structure of the cartilaginous tissue of the pinna are distinctive. Matching the distance of salient points on the pinna from a landmark location of the ear is the suggested method of recognition in this case.
Shortcomings:
This method is not believed to be very distinctive.
Odour – Each object spreads around an odour that is characteristic of its chemical composition and this could be used for distinguishing various objects.
Shortcomings:
I. Deodorants and perfumes could lower the distinctiveness.
Hand geometry – The essence of hand geometry is the comparative dimensions of fingers and the location of joints, shape and size of palm. The technique is very simple, relatively easy to use and inexpensive. Dry weather or individual anomalies such as dry skin do not appear to have any negative effects on the verification accuracy. Since hand geometry is not very distinctive it cannot be used for identification of an individual from a large population, but rather in verification. Further, hand geometry information may vary during the growth period of children and teenagers.
Shortcomings:
Not very distinctive, good for only the verification
Fingerprint – A fingerprint is a pattern of ridges and furrows located on the tip of each finger. Compact sensors provide digital images of these patterns. Fingerprint recognition for identification acquires the initial image through live scan of the finger by direct contact with a reader device that can also check for validating attributes such as temperature and pulse. In real-time verification systems, images acquired by sensors are used by the feature extraction module to compute the feature values.
Shortcomings:
I. One problem with the current fingerprint recognition systems is that they require a large amount of computational resources.
II. Fingerprints are susceptible to damage
Behavioural Features

4. Voice – The features of an individual's voice are based on physical characteristics such as vocal tracts, mouth, nasal cavities and lips that are used in creating a sound. These characteristics of human speech are invariant for an individual, but the behavioural part changes over time due to age, medical conditions and emotional state.
Shortcomings:
I. Subject to rapid non-permanent change
II. Multiple samples are required
Signature – The way a person signs his or her name is known to be characteristic of that individual. Collecting samples for this biometric includes subject cooperation. Signatures are a behavioural biometric that change over a period of time and are influenced by physical and emotional conditions of a subject and therefore are never truly constant. In addition to the general shape of the signed name, a signature recognition system can also measure pressure and velocity of the point of the stylus across the sensor pad.
Shortcomings:
I. Not very distinctive
II. Subject to rapid non-permanent change
Comparison of Biometrics Features
The table below contain a comparison of various biometric technologies
UNIVERSALITY
DISTINCTIVENESS
PERMANENCE
COLLECTABILITY
CIRCUMVENTION
Infrared thermogram
H
H
L
H
L
Odour
H
H
H
L
L
Ear
M
M
H
M
M
Hand geometry
M
M
M
H
M
Fingerprint
M
H
H
M
M
Face
H
L
M
H
H
Retina
H
H
M
L
L
Iris
H
H
H
M
L
Voice
M
L
L
M
H
Signature
L
L
L
H
H
DNA
H
H
H
L
L
(H-High, M-Medium, L-Low)
Sources:
(A SURVEY OF BIOMETRIC RECOGNITION METHODS; 46th International SyrnPoSium Electronics in Marine. ELMAR-2004. 16-18 June 2004. Zadar. Croatia; Kresimir Delac (HT - Croatian Telecom), Mislav Grgic (University of Zagreb))
(Biometrics: A Tool for Information Security; IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 1, NO. 2, JUNE 2006; Anil K. Jain, Fellow, IEEE, Arun Ross, Member, IEEE, and Sharath Pankanti, Senior Member, IEEE)
From all the information above, an ideal Biometric feature that could be used for an Electronic Voting System authentication is the Iris.
Listed below are reasons why the Iris is the best option;
1. Of all the biometric devices and scanners available today, it is generally conceded that iris recognition is the most accurate. Iris recognition is rarely impeded by glasses or contact lenses and can be scanned from 10cm to a few meters away.
2. The iris remains relatively stable and a sample from a single enrolment scan can effective for many years. Even blind people can use this scan technology since iris recognition technology is iris pattern-dependent and not sight dependent.
3. Iris scanning is an ideal way of biometric identification since the iris is an internal organ that is largely protected by damage and wear by the cornea. This makes it more attractive then fingerprints which can be difficult to recognize after several years of certain types of manual labour.
4. The iris is also mostly flat and controlled by 2 muscles so it helps make the iris movements more predictable then facial recognition. Even genetically identical twins have completely different iris patterns.
5. Iris capture cameras, in general, take a digital photo of the iris pattern and recreating an encrypted digital template of that pattern. That encrypted template cannot be re-engineered or reproduced in any sort of visual image. Iris recognition therefore affords the highest level of security of template.
6. The imaging process involves no lasers or bright lights and authentication is essentially non-contact. Today’s commercial iris capture cameras use infrared light to illuminate the iris without causing harm or discomfort to the subject.
And although there are many downsides to a biometrics recognition systems e.g. privacy, non-renewability, cost of implementation, susceptibility to attack, through the determination and commitment of the technology industry in

5. advancement and standardization, Iris recognition as an authentication concept is set to see a lot development, growth and mainstream acceptance.
Voting and E-Voting
Voting is an essential and critical element of any democratic consensus-based society. And of all its possible applications, National elections are the most important use of voting in democratic societies. Other applications of voting range from passing legislations to eliciting public opinion to holding referendums.
The voting process is made up of one optional and two fundamental modules;
As the size of population increased, conventional paper-based voting becomes burdensome for large-scale voting.
When compared to the conventional paper-based voting, e-voting has the following basic advantages:
 Convenience – E-voting is more convenient for voters. More polling booths can be set-up using remote connection for ballot collection. This reduces voters' travel time, and significantly increases voter turnout. Voters are also allowed to vote from any location at their convenience.
 Efficiency – Using some electronic means voting is executed quickly and efficiently. Ballots tabulation and the aggregation of results from different polling locations can be done with greater speed by implement appropriate media of transfer.
 Accuracy – Using e-voting, human error can be eliminated at the tallying stage. Ballot validity is automatically checked, and the counting is performed by software. Using certified software, the voting result obtained is more accurate compared to manual counting.
 Cost –The use of electronic ballot removes the cost of producing a physical ballot paper. The use of some remote communication mechanisms also minimises the cost of transporting physical ballots for aggregation of voting result. Ballot counting automation using a computer program minimises administration overhead, and reduces the number of officials required for the counting process.
 Security – Whether it is for fame, political power or financial gains there are considerable motives for cheating in voting. The challenge in e-voting research is to design a system providing more functionality and security than the current convention can provide.
Designing an Iris-Based Biometrics Electronics Voting System
Designing a biometrics based e-voting system that would not only be efficient and effective but also satisfy the voting. The idea I intend to explore below will simply provide an overview of a system that could be the answer to the electronic voting dilemma in authenticating eligibility of voters.
A Biometric system authentication process is made up of two (2) phases of with (3) modules.
In the first phase, the person is enrolled at which stage, the biometric data is acquired from him/her and used in create a data template. The template stored in a database or on a smart card and will provide the control sample with which any presented identity will be compared to.
In the second phase, the biometric system can be operated in two different modes;
 Identification: The user presents a biometric characteristic(s) to the biometric system through an input device. The presented characteristic(s) is used in creating a data sample which is then compared to all the identities
•Data about voters are collated to create a voter register of all eligible voters
•This is an optional module
Voters Registeration
•Voters make their choice from a series of optional responses to a pre-determined questionVote Casting
•The accumulated data is analyzed and an algorithm for deciding the winning option is executedVote CountingPHASE ONEPHASE TWO ENROLLMENTIDENTIFICATIONVERIFICATION

6. (data templates) in a database and if the presented biometric information is a positive match to any predefined data template, the user identity is established.
 Verification: The user presents a biometric characteristic(s) to the biometric system just as in the Identification mode. The only difference in this mode is that the user also claims an Identity and the data sample created from the presented characteristic(s) is only compared to the data template(s) corresponding to the claimed identity.
A biometric system must consist of four (4) major modules;
1. A Sensor Module: This module acquires the biometric data from the user
2. A Feature Extraction Module: This module controls the conversion of the acquired biometric data in a data template or sample by processing the acquired data to extract feature vectors (variables used in differentiating biometric features)
3. A Matching Module: This module compares the biometric data sample to the a predefined data template
4. A Decision Making Module: This module is the one in which the user’s identity is established or a claimed identity is accepted or denied based on the comparison performed in the Matching Module.
Iris-based Biometrics E-Voting System (IBES) is designed to accommodate identification and verification models resulting in two versions. Both have been represented below diagrammatically;
IBES I (IDENTIFICATION)
Advantage
 Comparison is made between a live sample and a template from a secure database
Disadvantage
 Longer matching latency as template/sample has to be delivered over a network
IBES II (VERIFICATION)
Advantage
 Short Matching latency as template is acquired before the sample
Disadvantage
 Integrity of template is not assured
 Loss of smart card results in increased cost of replacing them
Evaluation
The authentication system would be evaluated based on the following characteristics;
1. Failure to Acquire Rate (FtAR)): This is the ratio of numbers of biometric samples which have not been correctly acquired to the total number of acquisitions (Total number samples presented for acquisitions)

7. 2. Failure to Enroll Rate (FtER): The ratio of users that could not be enrolled to the total number of users presented for enrolment
3. False Acceptance Rate (FAR): The ratio of truly matched non-matching sample by the systems to the total number of tests(including FtAR and FtER)
4. False Reject Rate (FRR): The ratio of falsely unmatched matching samples by the systems to the total number of tests (including FtAR and FtER)
5. Equal Error Rate (ERR): The point on an error rate diagram where the FAR and FRR are equivalent
6. False Match Rate (FMR): The ratio of truly matched non-matching sample by the systems to the total number of tests (excluding FtAR and FtER)
7. False Non Match Rate (FNMR): The ratio of falsely unmatched matching samples by the systems to the total number of tests (excluding FtAR and FtER)
8. Ability to Verify Rate (AVR): The overall percentage of users that can be verified by a biometric system. The AVR can also be thought of as the combination of the FtER and the FRR. Mathematically, this relationship can be represented as follows:
AVR = [(1-FtER) * (1-FRR)]
9. Receiver Operating Characteristics (ROC): The diagram of a verification system where the FMR and FNMR specify the x and y axis respectively. An algorithm makes a decision based on the threshold which determines how close a sample and template must match for the sample to be considered a match. A higher threshold would reduce FAR but increase FRR and vice versa.
Conclusion
A biometric system for voter authentication will although will result in increased expenditure initially, the cost saving benefits over the long term will prove to be substantial. It will also resolve that the issue of ineligible voters or multiple voting influencing the results of an election but is by no means a silver bullet. However, it would result in a more peaceful voting process which further empowers the democratic electoral ideology.
References:
A SURVEY OF BIOMETRIC RECOGNITION METHODS; 46th International SyrnPoSium Electronics in Marine. ELMAR-2004. 16-18 June 2004. Zadar. Croatia; Kresimir Delac (HT - Croatian Telecom), Mislav Grgic (University of Zagreb)
Biometrics: A Tool for Information Security; IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 1, NO. 2, JUNE 2006; Anil K. Jain, Fellow, IEEE, Arun Ross, Member, IEEE, and Sharath Pankanti, Senior Member, IEEE)
Iris recognition border-crossing system in the UAE; John Daugman OBE, University of Cambridge and Imad Malhas, President and CEO, IrisGuard Inc.
Recognition of Human Iris Patterns for Biometric Identification; Report submitted as partial fulfilment of the requirements for the Bachelor of Engineering degree of the School of Computer Science and Software Engineering, The University of Western Australia, 2003; Libor Masek
An effective and fast iris recognition system based on a combined multiscale feature extraction technique; Pattern Recognition: The Journal of the Pattern Recognition Society(Received 1 May 2007; accepted 26 June 2007); Makram Nabti, Ahmed Bouridane (Institute for Electronics, Communications and Information Technology (ECIT), School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, Northern Ireland, UK) and Lahouari Ghouti (Department of Information and Computer Science, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia)
Modeling and Simulation of a Robust e-Voting System; Mohammad Malkawi, Associate Professor, Argosy University, and VP, AIM Wireless USA, Mohammed Khasawneh1, IEEE Senior Member, College of Engineering, University of Illinois at Urbana Champaign , Omar Al-Jarrah2, Associate Professor of Computer Engineer, Jordan University of Science & Technology, Laith Barakat, Jordan University of Science & Technology)
Iris Exchange (IREX) Evaluation 2008 (Concept, Evaluation Plan and API); IREX: An Evaluation-based Program for the Development of Exchangeable Iris Imagery and Support for Compact Interoperable ISO/IEC 19794-6 Records
Person Identification through IRIS Recognition; International Journal of Security and its Applications Vol. 3, No. 1, January, 2009; Poulami Das (Computer Science and Engineering Department, Heritage Institute of Technology, Kolkata - 700107, India),
Debnath Bhattacharyya (Computer Science and Engineering Department, Heritage Institute of Technology, Kolkata - 700107, India), Samir Kumar Bandyopadhyay(Department of Computer Science and Engineering, University of Calcutta, Kolkata - 700009, India); Tai-hoon Kim(Hannam University, Daejeon - 306791, Korea)
E-Voting and Biometric Systems; Sonja Hof (University of Linz, Austria Institute of Applied Computer Science, Division: Business, Administration and Society; University of Linz, AUSTRIA)
A Review of Vulnerabilities in Identity Management using Biometrics; 2010 Second International Conference on Future Networks; Fathimath Sabena, Ali Dehghantanha and Andrew.P.Seddon (Asia Pacific University College of Technology and Innovation, Technology Park Malaysia, Kualalumpor- Malaysia)
An introduction to biometrics: A concise overview of the most important biometric technologies; Keesing Journal of Documents & Identity, issue 17, 2006,Ravi Das
Iris-Based Biometric Cryptosystems; Christian Rathgeb and Ao.Univ.-Prof. Dr. Andreas Uhl (Department of Computer Sciences University of Salzburg, Jakob Haringer Str. 2 5020 Salzburg, AUSTRIA, Salzburg, November 2008)
IRIS Texture Analysis and Feature Extraction for Biometric Pattern Recognition; International Journal of Database Theory and Application; Debnath Bhattacharyya, Poulami Das (Computer Science and Engineering Department, Heritage Institute of Technology,Kolkata-700107, India), Samir Kumar Bandyopadhyay (Department of Computer Science and Engineering, University of Calcutta, Kolkata-700009, India) and Tai-hoon Kim(Hannam University, Daejeon – 306791, Korea)