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The Radio Frequency Identification (RFID) system is a technology for automated identification. Exploration of RFID technology dates back to 1948 when Harry Stockman published his research titled “Communication by means of the reflected power”. Unfortunately technologies such as integrated circuits, transistors and microprocessors were not yet available and RFID had to wait another 20 years for its first commercial application (Landt 2005). Between 1970 and 1980 several research laboratories and academic institutions carried out work on RFID implementations for animal tracking, theft prevention, item labelling and access control systems (Want 2006). Regardless of these applications, RFID systems remained obscure for many years. The first significant change to this occurred in the early nineties when companies across the world began to use RFID tags on a large scale due advancements in their energy efficiency and size reductions (Landt 2005).
Today’s systems are usually composed of either passive or active RFID tags and RFID readers. Active tags contain their own power source and thereby can transmit stronger signals and can be accessed from further distances. Most commonly they operate on the ultra-high frequency (UHF) band and can achieve up to 100 metres range depending on the surrounding environment (Weinstein 2005). There are currently two types of active tags. Transponders, also called semi-active tags, and Beacons. Transponders stay in “standby” mode until receiving signal from the reader and then transmit a signal back. Beacons emit signals and advertise their presence at pre-set intervals. Because of their on board power source, active tags are expensive, priced from $20 to $70 and vary in size from 2 centimetres upwards (Williams et al. 2014). Passive tags do not incorporate a power supply and are powered by the electromagnetic signal received from the reader through the tag’s antenna. They operate on low, high and ultra-high frequency with signals ranging up to 10 metres depending on the tag’s backscatter power (Weinstein 2005). The smallest passive tags can be size of a grain of rice and cost 1/10 of the price of the active tag (Williams et al. 2014).
Silva, Filipe and Pereira (2008) proposes a RFID based student attendance recording system that comprises of RFID readers operating at the 125 Kilohertz (KHz) frequency with an effective read range up to 10 – 15 centimetres and passive RFID tags embedded into plastic cards. The tags store a binary identifier which is unique to each student. Readers are connected to the local network with RJ45 connector through which they transfer scanned tag id to the server using the Transmission Control Protocol / Internet Protocol (TCP/IP). At least one reader is mounted in each of the classrooms and students need to take their card out and place it near the reader in order to register their attendance.
Nainan, Parekh and Shah (2013) claimed that a similar RFID attendance registration system setup decreased the time needed to record a student’s attendance by 98% compared to the manual entry method. Collected data shows that the RFID system was able to record the attendance of 5 students per second, however considering the short effective read range we have to conclude that multiple readers were used during that experiment to achieve such result. Despite advances over the paper based registers, efficiency of attendance systems based on passive RFID tags is limited by the number of readers located in the classroom. Analogous systems based on the active RFID technology could increase ids collection efficiency by scanning multiple tags simultaneously from a further distance (Yoon, Chung and Less 2008), however such systems would introduce a number of additional technological and social issues. Bandwidth limitations coerce RFID tags to share a common broadcast frequency and as a consequence multiple tags responding concurrently to the same reader can cause packet collisions. Therefore to solve these issues, advanced anti-collision algorithms and methods must be employed during development process (Bin, Kobayashi and Shimizu 2005). Increased reading range additionally raises serious privacy concerns as the user’s location could be tracked without their own consent (Ferguson, Thornley and Gibb, 2014).
Numerous properties must be satisfied to categorise the biological measurement of a human physiological or behavioural characteristic as biometrics. The characteristics should be unique, every person should have it and it needs to be accessible so it can be measured. There are a number of different studies exploring biometric authentication for attendance registration systems.
2.2.1 Voice recognition
Recent experiments by Dey et al. (2014) explore the capabilities of an attendance registration system based on voice recognition. The main core of the system is a Linux OS server integrated with a computer telephony interface (CTI) card and pre-installed with interactive voice response (IVR) software. The server is accessible only from the previously pre-defined phones which are installed in the classrooms. Using installed phones users have to record a reference voice sample to enrol into the system. During enrolment users are provided with a unique four digit speaker identification then they are asked to read for 3 minutes text of their own choice. Enrolled users can register their attendance by entering the previously received speaker identification number and then answering some simple random questions generated by the system. The system logs user attendance if the recorded speech matches the stored reference sample. Initial system evaluation performed on the group of 120 students indicated very low efficiency. In order to achieve 94.2% recognition rate, each user needs to produce at least a 50 seconds sample. Authentication time is additionally extended by an average 26 seconds computational time needed to analyse provided speech sample. Additional limitations come with the maximum number of 32 concurrent calls that each server can handle. In essence, a long compulsory enrolment process, the unnecessary burden of remembering a personal speaker identification number and the poor registration efficiency time make the system a poor candidate for large group registers.
According to Akinduyite et al. (2013) fingerprint attendance management systems can be more reliable and efficient than the voice based equivalent. They have achieved 97.4% recognition accuracy with an average registration time of 4.29 seconds per student. The system implements fingerprint scanners connected to a centralised server through the existing Wi-Fi infrastructure. As with the voice recognition system, an administrator has to capture reference fingerprint data from every user before the system can be used. Collected fingerprint templates are stored on the server in a Microsoft SQL Server database and later used to match scanned samples. Almost identical recognition rate of 98.57% was achieved by Talaviya, Ramteke and Shete (2013) in the similar fingerprint system setup. Analogous to the RFID based systems, the efficiency is closely related to the total number of the available scanners.
2.2.3 Automated Face recognition
All of the prior systems require users to provide a biometric sample manually by using one of the available scanners located in the environment. Kawaguchi et al. (2005) proposed a considerably different solution which automates sample collection. They introduced a face recognition method based on continuous observation. The system requires two cameras streaming live data to the centralized unit with preinstalled face detection and recognition software. The first camera, called the sensing camera is installed on the ceiling and points towards the room’s sitting area. The second camera, called the capturing camera is located in front of the seats to capture student’s faces. The sensing camera scans over the room in order to detect seats occupied by the students. Received image data is analysed using the Active Student Detecting (ASD) method developed by Nishiguchi et al. (2003). Once a student is detected, the system directs the capturing camera to the found location. The face image collected from the capturing camera is then processed by the system and the student’s attendance is recorded if a matching template is found. Experiments in which the described system was evaluated on a group of 12 students revealed 80% accuracy in engaged seats detection and the same level during face detection. The whole experiment took 79 minutes in which 8 scanning cycles were performed, resulting in 70% total accuracy for the attendance registering. Despite advances in automated biometric samples collection, the described system seems to be inefficient, especially if we consider time required to collect and analyse samples on such small group of students. Additional issues may arise if there are any obstructions in the room which can restrict the cameras view or if a low ceiling prevents sensing camera from covering the entire seating area.
The biometric systems have many advantages over the other authentication technologies. The biometric characteristics are tightly linked to the owner and can prevent identity theft, are difficult to duplicate and are very convenient as they are always available. Despite all these advances, all the biometric systems share serious ethical, social and security implications. It was evidenced by many researchers that there is a fear of biometric technologies on the whole. The individuals and potential system users are concerned about privacy, autonomy, bodily integrity, dignity, equity and personal liberty (Mordini and Tzovaras 2012; Kumar and Zhang 2010). The system administrators have additional overhead with the security of the collected biometric data. The individual biometric characteristic cannot be replaced if they get stolen, therefore the legal responsibilities whilst storing this kind of data are colossal.
An interesting and novel attendance registration method was proposed by Choi, Park and Yi (2015). The authors created a system which incorporates Wi-Fi technology built into smartphone devices. They had developed two versions of a smartphone application, one for the lecturers and one for the students. When a class session starts the lecturer has to create a Wi-Fi Access Point (AP) using his version of the application. The students attend the lecture and scan for the available Wi-Fi Access Points and if the lecturer’s AP is discovered and student’s device stays in its range for specified amount of time then attendance registration process is triggered. To overcome limitations with the maximum number of concurrent connections that single AP can handle, the created student’s version scans only for the nearby networks but never connects to the found AP’s. Attendance is registered by submitting a Message Digests 5 (MD5) hash token that combines a Service Set Identifier (SSID) of the found AP and student’s smartphone Media Access Control (MAC) address. The hash token is uploaded to the server which verifies submitted data and registers the student’s attendance in the local store. The system architecture requires collection of the reference MAC address of all the students for the purpose of the later validation. The study does not describe what smartphone models were used throughout the experiment, but it seems that they did not consider privacy features on iOS devices. According to Apple (2013), since the release of iOS 7.0, the MAC identifier is no longer accessible through third party applications, moreover after iOS 8.0 release, real device MAC address is hidden from the access points and swapped with a randomly generated one (Apple 2015 A). Taking into account that over 98% of iOS devices run on iOS 7.0 and above (Apple 2015 B), only confirms that the proposed system design should be reviewed again.
2.4.1 QR Code with face recognition
Fadi and Nael (2014) combined biometrics with Quick Response Codes (QR). The proposed methodology requires lecturers to generate a unique QR code and display it in the class. In order to register their attendance, students need to download a mobile application, install it on their smartphones and use it to scan the presented QR code. The scanned code is then submitted to the server via the existing University Wi-Fi infrastructure. Furthermore the application performs an identity check by scanning the student’s facial image which is later used to create matching score by analysing a reference image stored on the servers. Lecturer can manually validate submitted images to confirm a student’s identity if a low matching score raises any concerns. The QR code image could be effortlessly forwarded to other students outside the classroom, therefore the system also collects a location stamp on the code submission. The apparent vulnerability of the system lies in the number of technologies that it depends on. Authors assumed that every student will have a smartphone device with front and back facing cameras for the facial images and the QR scans and also a Global Positioning System (GPS) module which will be accessible during the registration stage. Each classroom has to be also equipped with a large screen to present codes to the students and this may not always be available.
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