Automated Monitoring Attendance System Essay

1.1 The caper and its scopeIn this paper we shoot for a system that automates the whole process of taking attention and maintaining its records in an academic institute. Managing people is a difficult task for more or less of the organizations, and maintaining the attention record is an important factor in people management. When considering academic institutes, taking the attending of disciples on daily basis and maintain the records is a major task. Manu all toldy taking the attendance and maintaining it for a long time adds to the impediment of this task as well as waste a drove of time.For this reason an efficient system is designed. This system takes attendance electronically with the wait on of a fingerbreadth crisscross sensor and all the records be saved on a computer server. Fingerprint sensors and liquid crystal display screens ar domiciled at the entrance of each(prenominal) room. In order to mark the attendance, student has to place his/her thumb on the reproduce sensor. On identification students attendance record is updated in the database and he/she is notified by dint of LCD screen. No lease of all the stationary material and eespecial(a)(a) personal for donjoning the records. Furthermore an automated system replaces the manual of arms system.1.2 IntroductionNowadays, industry is experiencing umpteen technological advancement and changes in modes of learning. With the rise of globalization, it is becoming essential to decide 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 ancient way. In a certain way, one and only(a) thing that is still in manual process is the recording of attendance. After having these issues in mind we invent an Automated Monitoring Attendance clay, which automates the whole process of taking attendance and maintaining it, plus it holds an accurate records.Biometric systems nominate been astray utilise for the purpose of cite. These learning methods refer to automatic quotation of people establish on the approximately specific physiological or behavioral disports 1. There are many biostatistics that whoremaster be utilized for some specific systems entirely the key structure of a biometric system is always same 2. Biometric systems are basically use for one of the two objectives identification 3 or slip awayicap 4. designation means to find a match between the research biometric test and the one that is already been stored in database 5. For example to pass through a restricted area you may have to s bottom of the inning your finger through a biometric cheat. A new template bequeath be generated that allow be thusly compared with the previously stored templates in database. If match found, then the person ordain be al milded to pass through that area.On the some other hand chit means the process of checking whether a query biometric sampl e belongs to the claimed identity or not 6. Some of the most comm only(prenominal) 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 fingermark recognition technique 1 for obtaining students attendance. Human beings have been utilise reproduces for recognition purposes for a very long time 7, because of the simplicity and truth of reproduces.Finger print identification is based on two factors (i) exertion the basic characteristics and features do not change with the time. (ii) Individuality fingerprint of each person in this world is unique 8. Modern fingerprint co-ordinated techniques were initiated in the late 16th century 9 a nd have added most in 20th century. Fingerprints are considered one of the most mature biometric technologies and have 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 get a line the students.1.3 Theoretical BackgroundFor over 100 years, fingerprint has been used to pick up people. As one of the biometric identification, fingerprint is the quite the most popular one. anyway getting the print for fingerprint is easy, it doesnt need a special sophisticated hardware and software to do the identification. In the old multiplication 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.D uring the fingerprint twin(a) process, the rooftrees of the two fingerprints go out be compared. at any rate using covers, some of the identification techniques also use minutiae. In brief, minutiae scum bag be described as point of interest in fingerprints. many an(prenominal) types of minutiae have been defined, such as pore, delta, island, ridge ending, bifurcation, spurs, bridges, crossover, etc, that commonly only two minutiae are used for their stability and robustness (4), which are ridge ending and bifurcation.To help in fingerprint identification, fingerprint smorgasbord method is devoured. There are some variety theories applicable in the existent world such as The NCIC System (National Crime Information Center) passive used even until now, the NCIC system classifies fingers according to the combination of builds, ridge awaits, ringlet tracing. NCIC determines.Fingerprint sorting (FPC) issue codes to represent the fingerprint characteristics. The following are the field codes plug-insUsing NCIC system FPC Field Codes eliminates the need of the fingerprint image and, thus, is very helpful for the need 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 henry and American Classification Systems henry and American classification systems, although has a attraction in common, are actually two different systems developed by two different people. The hydrogen 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 field thumb denoted count 1, right little left finger denoted number 5, left thumb denoted number 6, and lastly the left little finger denoted number 10.According to Henry System, there were two classifications the original and the secondary. In the primary classification, it was a paradiddle that gives the finger a value. While even numbered fingers were treated as the nominator, leftover numbered fingers were treated as denominator. severally fingers value was concern 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 arch (A)). For other fingers except those two index fingers, they were all assigned with itty-bitty letter which was also known as small letter group. Furthermore, a sub 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 going away 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 accordingly. 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 examine the source 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, meddling time and reduced computational complexity.The first known study of fingerprint classification was proposed by in 1823 by Purkinje, which resulted in fingerprin t classification master into 9 categories transverse curve, central longitudinal strain, cata-cornered stripe, oblique loop, almond whorl, spiral whorl, ellipse, circle, and double whorl. Later on, more in abstruseness 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 implement all other classification systems. What we need to do is to see what chassis of classification pattern the algorithm can distinguish.Fingerprint MatchingIn general, fingerprint matching can be categorized down into 3 cate gories Correlation-based matching the matching process begins by superimposing (lying over) two fingerprints, and calculating the correlational statistics between both 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. Ridge 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 anaesthetic 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 observe pr oblem. 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 attendance 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 part Hardware/ software system Design, Rules for marking attendance and Online Attendance cut across. Each of these is explained below. 2 System Description2 .1 HardwareRequired hardware used should be easy to maintain, implement and slowly available. Proposed hardware consists following move(1) Fingerprint Scanner(2) LCD Screen(3) figurerFingerprint electronic scanner pass on be used to input fingerprint of teachers/students into the computer software. LCD display will be displaying rolls of those whose attendance is marked. Computer Software will be interfaci ng fingerprint scanner and LCD and will be attached 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 students. 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 received 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 ma nually 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 field 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 local area networkWe are using LAN for communication among serve rs and hard wares in the classroom. We can instead use wireless LAN with portable devices. Portable device will have an embedded fingerprint scanner, wireless connection, amicroprocessor implike with software, memory and a display terminal.Source/References1 D. Maltoni, D. Maio, A. K. Jain, S. Prabhaker, Handbook of Fingerprint course credit, 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 engineering, IEEE Transactions on Volume 14, Issue 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 epitome 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 Communications Research Laboratory (MCRLab) tutor 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 Printing Office, Washington, D. C., 1984. 10 H. C. Lee and R. E. Gaensslen (eds.), Advances in Fingerprint Technology , molybdenum Edition, CRC Press, New York, 2001. 11 K.G.M.S.K. Jayawardana, T.N. Kadurugamuwa, R .G. Rage and S. Radhakrish nan , Timesheet An Attendance Tracking System , minutes of the Peradeniya University Research Sessions, Sri Lanka, Vol.13, Part II, 18th December 2008 .12 Yohei KAWAGUCHI, Tetsuo SHOJI , Weijane LIN ,Koh KAKUSHO , Michihiko MINOH , Face Recognition based Lecture Attendance System , Department of Intelligence Science and Technology, ammonia alum School of Informatics, KyotoUniversity. Academic Center for Computing and Media Studies, Kyoto University. 13 Digital Persona, Inc. t720 Bay street 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 Four4.1 Summary4.2 inference and Recommendation4.3 BibliographySource/References