Automated Monitoring Attendance System Essay

1.1 The problem and its scopeIn this paper we propose a formation that automates the whole process of taking at decenniumding and maintaining its records in an academic institute. Managing people is a difficult task for most of the organizations, and maintaining the attending record is an important factor in people management. When considering academic institutes, taking the attendance of learners on daily basis and maintain the records is a major task. Manually taking the attendance and maintaining it for a long quantify adds to the difficulty of this task as well as waste a lot of time.For this reason an efficient system is designed. This system takes attendance electronically with the help of a fingermark demodulator and all the records ar saved on a computer server. Fingerprint sensors and LCD screens are placed at the entrance of separately room. In tramp to mark the attendance, assimilator has to place his/her thumb on the fingerprint sensor. On identification stude nts attendance record is updated in the database and he/she is notified through and through LCD screen. No posit of all the stationary material and special personal for keeping the records. Furthermore an automated system replaces the manual of arms system.1.2 IntroductionNowadays, effort is experiencing many technological advancement and changes in methods of learning. With the rise of globalization, it is becoming essential to find an easier and more effective system to help an organization or company. In spite of this matter, there are still business establishments and schools that use the old-fashioned way. In a certain way, one thing that is still in manual process is the recording of attendance. After having these issues in mind we develop an Automated Monitoring Attendance brass, which automates the whole process of taking attendance and maintaining it, plus it holds an precise records.Biometric systems strike been widely used for the purpose of recognition. These recogni tion methods refer to automatic recognition of people establish on the some specific physiological or behavioral features 1. There are many biometry that can be utilized for some specific systems but the key structure of a biometric system is always same 2. Biometric systems are basically used for one of the two objectives identification 3 or verification 4. Identification means to find a first mate between the query biometric sample and the one that is already been stored in database 5. For example to pass through a restricted area you may have to run out your finger through a biometric guile. A new template get out be generated that ordain be thus compared with the previously stored templates in database. If match found, then the person will be allowed to pass through that area.On the other hand verification means the process of checking whether a query biometric sample belongs to the claimed identity operator or not 6. Some of the most commonly used biometric systems are (i) Iris recognition, (ii) Facial recognition,(iii)Fingerprint identification, (iv) Voice identification, (v) DNA identification, (vi) Hand geometry recognition and (viii)Signature Verification 5.Previously the biometrics techniques were used in many areas such as building security, ATM, credit cards, criminal investigations and passport control 4. The proposed system uses fingerprint recognition technique 1 for obtaining students attendance. Human beings have been use fingerprints for recognition purposes for a very long time 7, because of the simplicity and accuracy of fingerprints.Finger print identification is based on two factors (i) Persistence the basic characteristics and features do not change with the time. (ii) Individuality fingerprint of every person in this world is unique 8. mod fingerprint matching techniques were initiated in the late 16th century 9 and have added most in 20th century. Fingerprints are considered one of the most mature biometric technologies and h ave been widely used in forensic laboratories and identification units 10. Our proposed system uses fingerprint verification technique to automate the attendance system. It has been proved over the years that fingerprints of each every person are unique 8. So it helps to uniquely identify the students.1.3 Theoretical backdropFor over 100 years, fingerprint has been used to identify people. As one of the biometric identification, fingerprint is the quite the most popular one. Besides getting the print for fingerprint is easy, it doesnt need a special sophisticated computer hardware and software to do the identification. In the old times and even until now, fingerprints are usually taken using merely inks and papers (could be one print, ten prints, or latent print). Finger print is unique. There is no case where two fingerprints are found to be exactly identical.During the fingerprint matching process, the ridges of the two fingerprints will be compared. Besides using ridges, some of the identification techniques also use minutiae. In brief, minutiae can be described as point of elicit in fingerprints. Many types of minutiae have been defined, such as pore, delta, island, ridge ending, bifurcation, spurs, bridges, crossover, etc, but commonly only two minutiae are used for their stability and robustness (4), which are ridge ending and bifurcation.To help in fingerprint identification, fingerprint classification method is apparatused. There are some classification theories applicable in the received world such as The NCIC System (National Crime Information meaning) Still used even until now, the NCIC system classifies fingers according to the combination of conventions, ridge counts, whorl tracing. NCIC determines.Fingerprint miscellanea (FPC) field codes to represent the fingerprint characteristics. The adjacent are the field codes skirtsUsing NCIC system FPC Field Codes eliminates the need of the fingerprint image and, thus, is very helpful for the nee d of fingerprint identification for those who do not have access to an AFIS. Instead of relying to the image, NCIC relies more on the finger image information. The atomic number 1 and American sort Systems hydrogen and American classification systems, although has a lot in common, are actually two different systems developed by two different people. The Henry Classification System (5) was developed by Sir Edward Henry in 1800s used to record criminals fingerprints during Civil War. Henry System used all ten fingerprints with the right thumb denoted number 1, right little left finger denoted number 5, left thumb denoted number 6, and lastly the left little finger denoted number 10. concord to Henry System, there were two classifications the primary and the secondary. In the primary classification, it was a whorl that gives the finger a value. While even numbered fingers were treated as the nominator, peerless numbered fingers were treated as denominator. Each fingers value was eq ual to the value of the whorl plus one. In the secondary classification, each hands index finger would be assigned a special capital letter taken from the pattern types (radial loop (R), tented loop (T), ulnar loop (U), and severe (A)). For other fingers except those two index fingers, they were all assigned with small letter which was also known as small letter group. Furthermore, a shade secondary classification existed it was the grouping of loops and whorls, which coded the ridge of the loops and ridge tracings of whorls in the index, middle, and ring fingers. The following is the table of Henry System.The American Classification System was developed by Captain James Parke. The difference lies in assigning the primary values, the paper used to file the fingerprint, and the primary values calculation.Filing SystemsIn this system, all of the fingerprints are stored in cabinets. Each cabinet contains one different classification and, thus, the fingerprint cards are stored accordi ngly. The existence of AFIS system greatly helps the classification process. There is no need to even store the physical fingerprint cards. AFIS does not need to count the primary values of all those fingers and does not have to be as complicated as NCIC System. With the power of image recognition and classification algorithm, fingerprint identification can be done automatically by comparing the come digital image to the target database containing all saved digital images. Another important issue to know is the fingerprint classification patterns. These patterns are growing with each generation of AFIS and differ from one too to another, searching time and reduced computational complexity.The first known study of fingerprint classification was proposed by in 1823 by Purkinje, which resulted in fingerprint classification down into 9 categories transverse curve, central longitudinal strain, oblique stripe, oblique loop, almond whorl, spiral whorl, ellipse, circle, and double whorl. afterwards on, more in depth study was conducted by Francis Galton in 1892, resulted in fingerprint classification down into 3 major classes arch, loop, and whorl. Ten years later, Edward Henry refined Galtons experiment, which was later used by many law enforcement agencies worldwide. Many variations of Henry Galtons classification schemes exists, however there are 5 most common patterns arch, tented arch, left loop, right loop, and whorl. The following are types of fingerprint classification patternsSince IDAFIS is another extended form of AFIS, we do not need to utensil all other classification systems. What we need to do is to see what kind of classification pattern the algorithm can distinguish.Fingerprint MatchingIn general, fingerprint matching can be categorized down into three categories Correlation-based matching the matching process begins by superimposing (lying over) two fingerprints, and conniving the correlation between both(prenominal) by taking displacement (e.g . translation, rotation) into account. Minutiae based matching Minutiae are first extracted from each fingerprint, aligned, and then calculated for their match. rooftree feature based matching Ridge patterns are extracted from each fingerprint and compared one with another. The difference with minutiae based is thatinstead of extracting minutiae (which is very difficult to do to low quality fingerprint image) ridge pattern such as local orientation and frequency, ridge shape, and texture information is used.Chapter TwoMost of the attendance systems use paper based methods for taking and calculating attendance and this manual method requires paper sheets and a lot of stationery material. Previously a very few work has been done relating to the academic attendance monitoring problem. Some softwares have been designed previously to keep track of attendance 11.But they require manual entry of data by the staff workers. So the problem remains unsolved. Furthermore idea of attendan ce tracking systems using facial recognition techniques have also been proposed but it requires expensive apparatus still not getting the required accuracy 12. Automated Monitoring Attendance System is divided into three parts Hardware/ computer software Design, Rules for marking attendance and Online Attendance Report. Each of these is explained below. 2 System Description2 .1 HardwareRequired hardware used should be easy to maintain, implement and easily available. Proposed hardware consists following parts(1) Fingerprint Scanner(2) LCD Screen(3) ComputerFingerprint scanner will be used to comment fingerprint of teachers/students into the computer software. LCD display will be displaying rolls of those whose attendance is marked. Computer Software will be interfacing fingerprint scanner and LCD and will be connected to the network. It will input fingerprint, will process it and extract features for matching. After matching, it will update the database attendance records of the st udents. A fingerprint sensor device along with an LCD screen is placed at the entrance of each classroom. The fingerprint sensor is used to capture the fingerprints of students while LCD screen notifies the student that his/her attendance has been marked.2 .2 Rules for marking attendanceThis part explains how students and teacher will use this attendance management system. Following points will make sure that attendance is marked correctly, without any problem (1) All the hardware will be outside of the classroom.(2) When teacher enters the classroom, the attendance marking will start. Computer software will start the process after inputting fingerprint of the teacher. It will find the Subject ID and current semester using the ID of the teacher or could be set manually on the software. If the teacher doesnt enter the classroom, attendance marking will not start. (3) After some time, say 15 minutes of this process. The student who login after this time span will be marked as late on the attendance. This time period can be increased or decreased per requirements.2 .3 Online Attendance ReportDatabase for attendance would be a table having following fields as a combination for primary field (1) Day, (2) Roll, (3) Subject and following non-primary fields (1) Attendance, (2) Semester. Using this table, all the attendance can be managed for a student. For online report, a simple website will be made for it. Which will access this table for showing attendance of students .The sq queries will be used for report generation? Following query will give total numbers of classes held in a certain subject. Now the attendance percent can easily be calculated2.4 Using wireless network instead of LANWe are using LAN for communication among servers and hard wares in the classroom. We can instead use wireless LAN with portable devices. Portable device will have an imbed fingerprint scanner, wireless connection, amicroprocessor loaded with software, memory and a display terminal.S ource/References1 D. Maltoni, D. Maio, A. K. Jain, S. Prabhaker, Handbook of Fingerprint Recognition, Springer, New York, 2003.2 A.C. Weaver, Biometric authentication, Computer, 39(2), pp 96 97 (2006). 3 J. Ortega Garcia, J. Bigun, D. Reynolds and J.Gonzalez Rodriguez, Authentication gets personal with biometrics, Signal Processing Magazine, IEEE, 21(2), pp 50 62 (2004).4 Anil K. Jain, Arun Ross and Salil Prabhakar, An introduction to biometric recognition , Circuits and Systems for Video technology, IEEE Transactions on Volume 14, curve 1, Jan. 2004 Page(s)4 20. 5 Fakhreddine Karray, Jamil Abou Saleh, Mo Nours Arab and Milad Alemzadeh, Multi Modal Biometric Systems A State of the Art Survey , Pattern Analysis and Machine Intelligence Laboratory, University of Waterloo, Waterloo, Canada. 6 Abdulmotaleb El Saddik, Mauricio Orozco, Yednek Asfaw, Shervin Shirmohammadi and Andy Adler A Novel Biometric System for Identification and Verification of Haptic Users , Multimedia Commu nications Research Laboratory (MCRLab) School of Information Technology and Engineering University of Ottawa, Ottawa, Canada .7 H. C. Lee and R. E. Gaensslen, Advances in Fingerprint Technology , Elsevier, New York . 8 Sharath Pankanti, Salil Prabhakar, Anil K. Jain, On the Individuality of Fingerprints , IEEE transaction on pattern analysis and machine intelligence, vol.24, no.8, August 2002. 9 Federal Bureau of Investigation, The Science of Fingerprints Classification and Uses , U. S. government activity Printing Office, Washington, D. C., 1984. 10 H. C. Lee and R. E. Gaensslen (eds.), Advances in Fingerprint Technology , Second Edition, CRC Press, New York, 2001. 11 K.G.M.S.K. Jayawardana, T.N. Kadurugamuwa, R .G. Rage and S. Radhakrishnan , Timesheet An Attendance Tracking System , Proceedings of the Peradeniya University Research Sessions, Sri Lanka, Vol.13, situation II, 18th December 2008 .12 Yohei KAWAGUCHI, Tetsuo SHOJI , Weijane LIN ,Koh KAKUSHO , Michihiko MINO H , Face Recognition based Lecture Attendance System , Department of Intelligence Science and Technology, Graduate School of Informatics, KyotoUniversity. Academic Center for Computing and Media Studies, Kyoto University. 13 Digital Persona, Inc. t720 Bay road Redwood City, CA 94063 USA 5, http//www.digitalpersona.comTable of ContentsChapter One1.1 The problem and its scope1.2 Introduction1.3 Theoretical BackgroundChapter Two2.1 Hardware and Software2.2 Rule for marking attendance2.3 Online Attendance Report2.4 Using Wireless network instead of LANChapter Three.Chapter quad4.1 Summary4.2 Conclusion and Recommendation4.3 BibliographySource/References