Biometric Attendance System IEEE2011

7/31/2019 Biometric Attendance System IEEE2011

1/3

Poceedigs of the 201 1 EEECMEteatioal Cofeece o Complex Medical Egieeig

May 22 - 25,Habi, Chia

Biometric Attendance SystemEngr. Imran Anwar Ujan and Dr. Imdad Ali Ismaili

Institute of Information Communication Technolo,

Universi ofSindh, Jamshoro, Sind Pakistan

Pakistan

bsc - Th h k h f dym f my' d ud Th ym f u! z mk d dvdu m h d fm hum m u f h ym y M d dh h ym h d f d hu d u

I. MS AND BJECTVE

The aim of this system is to implement in C#.net set ofreliable tecniques for ngerprint image enhancement andminutiae extraction. The perfomance of these techniques willbe evaluated on a ngerprint data set.

In combination with these development techniques, statisticalexperiments can then be performed on the ngerprint data set.The results om these experiments can be used to help usbetter understand what is involved in determining thestatistical uniqueness of ngerprint minutiae.

The main aim that this system would test whetherattendance by ngerprint is enough for identication. It isexpected that the work in this system will reach the stage ofbeing able to lly test hypothesis.

II. ACKGROUND/ONTEXT

Fingerints are the oldest form of biometric identication.Mode ngerprint based identication is used in forensicscience, and in biometric systems such as civilianidentication devices. Despite the widespread use ofngerprints, there is little statistical theory on the uniquenessof ngerprint minutiae.

A ngerprint is formed om an impression on a surface ofcomposite curve segments. A ridge is dened as a singlecurved segment, and a valley is the region between twoadjacent ridges. The minutiae, which are the localdiscontinuities in the ridge ow patte, provide the details ofthe ridge-valley structures, like ridge endings and bircations.There are 50 to 150 minutiae on a single ngerprint image.Features such as the type, direction, and location of minutiaeare taken into account when performing minutiae extraction

The work of F.Galton dened a set of Galton Features for

ngerprint identication, which since then, has been renesand extended to include additional types of ngerprintfeatures. However, most of these features are not used inautomatic ngerprint identication systems. Instead the set ofminutiae types are restricted into only two types, ridge endingsand bircations, as other types of minutiae can be expressedin terms of these two features types. Ridge endings are thepoints where the ridge curves terminates, and bircations arewhere a ridge splits om a single path to two paths at a -

978-1-4244-9324-1/11/$26.00 2011 IEEE 499

junction. In this research, we will be dealing mainly with ridgeendings and bircations.

There are various types of approaches proposed inliterature for both image enhancement, and minutiaeextraction om ngerprints. The literature on these techniqueswill be examined are reviewed in determining the bestapproach to develop for this research. In particular, thengerprint image enhancement algorithm employed by Honget al. will be evaluated and implemented to understand howthe enhancement algorithm works and how well it performs.Once a reliable minutiae extraction technique has beenimplemented and tested, this can be used as the basis ofstatistical alysis of ngerprint minutiae.

The work of Tu and Harley and Pankanti et al. can beexamined in which a statistical amework for analyzingsystem performance has been presented. Tu and Hartleydened a means of forming a binary code om set ofngerprint features and then performing a set of matchingexperiments on the database to estimate the number of degreesof eedom within the ngerprint poplation.

ProblemThere are some problems which face by ngerprintrecognition or identication security. We can catch a cold bytouching a biometric system (ngerprint).

Twins have identical biometric traits (identical

ngerprints, irises ... ). This is the same clones.

Stolen body parts can be reused.

Biometric features can be reconstructed om the

template.

Making a fake nger is easy.

The inability of ngerprint systems to enoll children andsmall Asian women.

III. ROJECT SPECFCATON

A Project Description

The goal of this project is to daily attendance of employee

tough ngerprint. The project is design and implementssoware architecture for ngerprint analysis. The systemshould be able to extract key features om a scannedngerprint image and to compare these with a database ofknown ngerprint images and/or extracted feature sets.

For this project we provided with a set of previouslyacquired ngerprints and a working ngerprint sensor withdriver soware for Windows. Our expectation had llled bymost of the algorithm development which executed in C# dotnet ad this work done on a Windows PC.


2/3

B Project Task

The project can be split to a set of prnciple tasksrepresenting a progression towards the end goal of a workingngerprnt analysis system.

1. We had must reviewed tecniques for analyzing

ngerprints and performing patte recognition on

sets of ngerprints. Several of the most promising

algorithms/ tecniques had implemented in C# .net

and initial testing performed on the test set of

ngerprint image provided.

2. The Biomeric Attendance System soware

rchtecre or he mi sste w desiged; he

main subsystems required were determined and a

method of implementing a full system was evaluated

work architecture and several of the nctional

subsystems were implemented.

3. We analyzed algorithms were implemented and

integrated with the ngerprint sensor, and real-time

acquisition and analysis of a ngerint was

demonstrated an improvements in processing speedand implemented and demonstrated.

4 Improvements in the analysis of an acquired image

may be achieved though image processing

combining multiple acquired images to provide an

eanced composite image or more sophisticated

statistical or mathematical approaches.

5. Improvements in patte matching may be achieved

though various patte recognition approaches the

students should evaluate several approaches,

developing an evaluation methodology which enables

a comparison in terms of improved recognition and a

reduction in terms of false positives and negatives.

Integrating the techniques of 4 and 5 with the real-timeacquisition of ngerprints will add signicant bonus value.

C Project PlanningEffective management of a soware project depends on

thoroughly planning the progress of the project. Weanticipated problems which arose and prepared solutions tothe project problems. A plane, drawn up of a project, we usedas the driver for the project. The initial plane evolves as theproject progress and better information.

The planning process starts with an assessment of the

constrains (required delivery date, overall budget, etc)affecting the project. This is carried out in conjunction with anestimation of project parameters such as its structure, size, anddistribution of functions. The progress milestones anddeliverables are then dened. The process then enters a loop.A schedule for the project is drawn up and the activitiesdened in the schedule are initiated or given permission tocontinue. Aer some time usually about 2-3 weeks, progressis reviewed and discrepancies noted. Because initial estimates

500

of project parameters are tentative, the plan will always needto be modied.

A structure of BAS soware development plan isdescribed below

IV. NTRODUCTON

Here we describe the bref objectives of the BAS projectand set out the constraints which aect the project

management.A Objectives

Computerize the daily attendance system

Attendance by ngerprint

Protect the proxy which IS doing daily

attendance

B Constraints Budget

C Methods

1. Veridicom Fingerprint Sensor RS.8,500

2. Expenditure for collection of data and

information Rs.2500

3. Total budget is Rs. 11,000

A rough task breakdown for this project is as follows: Examine and review available literature on image

enhancement and minutiae extraction techniques.

Develop a series of image enhancement techniques to

aid the minutiae extraction process.

Develop a set of reliable techniques to extract the

minutiae om ngerprint images.

Evaluate the performance of the techniques using the

ngerint data set.

Use existing teciques as the benchmark forcomparng the performance of the technique

developed.

Aer reliable minutiae detection techniques have

been developed and tested, then statistical analysis

experiments on the ngerprint data set can be

performed and documented.

Emy P

1

Adc T


3/3

(Fl)

(a). Flow Cha of Fingerpt

Dtbue ingrprnt

(b). Flow Cha of Fingerpit Mtcng (Attndnc

Fig.2 ERD Model & Flow Charts of Systems

D. Project Evalution

The primary focus of the work in this project is on theenhancement of ngerprint images, and the subsequentextraction of minutiae.

Firstly, we have implemented a series of techniques forngerprint image enhancement to facilitate the extraction ofminutiae. Experiments were then conducted using acombination of both synthetic test images and real ngerprintimages in order to provide a well-balanced evaluation on theperformance of the implemented algorithm. The use of

synthetic images ha provided a more quantative measures ofinspection, but can provide a more realistic evaluation as theyprovide a natural representation of ngerprint imperfectionssuch as noise and copted elements. The experimentalresults have shown that combined with an accurate estimationof the orientation and ridge equency the Gabor lter is ableto eectively enhance the clarity of the ridge structures whilereducing noise. In contrast, for low quality images that exhibithigh intensities of noise, the lter is less effective inenhancing the image due to inaccurate estimation of the

50 1

orientation and ridge equency parameters. However, inpractice, this does not pose a signicant limitation asngerprint matching techniques generally place moreemphasis on the well-dened regions, and will disregard andimage if it is severely corrupted.

Overall, the results have shown that the implementedenhancement algorithm is a useful step to employ prior tominutiae extraction. The Crossing Number method was then

implemented to perform extraction of minutiae. Experimentsconducted have shown that this method is able to accuratelydetect all valid regions, and will disregard an image if it isseverely corrupted.

However, thr are cse wher the xtracted minutia donot correspond to true minutiae. Hence, an image postprocessing stage is implemented to validate the minutiae. Theexperimental results om the minutiae validation algorithmindicate that this additional post processing stage is effectivein eliminating various types of false minutiae structures.In combination with the implemented techniques for imageenhancement and minutiae extraction, preliminaryexperiments on the statistics of ngerints were conducted on

a sample set of ngerprint images. Tee types of statisticaldata were collected, which include minutiae density, distancebetween neighboring minutiae, and ridge wavelength.

Overall, we have implemented a set of reliable tecniquesfor ngerint image enhancement, minutiae extractionngerprint matching and classication. These techniques weimplemented for employer daily attendance system. Troughwhich employers attend you by ngerprint only enter theiremployer ID and put his nger on sensor.

V. ONCLUSON

Our project "Biometric Employer Attendance System(BEAS) is an extensible work for any organization orcompany in this fast world. Keeping the view of research stillthere is a lot of improvement work and exibility for thecoming technologies in the various demanding directions.The language which we have is very vast and even the underMicroso products is trying to rule over the InformationTecnology, So we hope that this project will be the point ofinterest for our successors to be enhanced rther to market itcompatible with the demands of the organizationrequirements.

EFERENCES

[I] Amengual, J.C, Juan, A, Perz, J. ., Prat, F, SEZ, S, and Villar, J, M,"Real time minutiae extraction in ngerprint images, proceedings of6th nteational conference on mage Processing d its Applications ,

Jully 1997,pp 87 1-875[2] Dankmeijer, J., Waltman, J.M, d Wild, G.D "Biological foundationsfor forensic identication based on ngerprints. Acta Morphological

Neerlando scandivncia 18,1 (1980),67-83[3] Guo, Z, nd Hall, R.W "Parallel thinning with two-sub iteration

algorithms, communications of the ACM 32,3 (March 1989),359-373.[4] Hong, L, Wan, Y and Jain, A.K.Fingerprint image enhancement:

Algorithm and performance evaluation, EEE transactions on PatteAnalysis and Machine ntelligence 20, 8(1998),777-789.

Documents

Biometric Attendance System IEEE2011