Design and Analysis
Discuss about the Iot Based Wireless Attendance Management System Using FInger Print Recognition.
This chapter deals with the hardware implementation and the software components of the system. System flow chart deals with the working flow of this project. The detailed explanation is given for the implementation of this project with the components specification.
Here we have two block diagram, one works with the Raspberry Pi and the other works with the Arduino module. In the beginning the fingerprint of all the students present in the class should be stored in the server that is to be connected in the Raspberry Pi. This is done in order to realize the student present in that particular class. With the help of the enroll button (E) present in the Raspberry Pi, the student list is been stored in the database. This should be done for each and every student a particular class. Now, before starting the process the faculty member has to press the start button (ST). During this process a LED, which is green in color starts to glow. Then the students have to press the Identification (I) button before he places his/her fingerprint. This activity is done until each and every student places his/her finger print. After the completion of this process the faculty member have to finish this process by pressing Stop (SP) button. This is denoted by the LED of red color. The start, stop, identification and enrollment are done in the Raspberry Pi and the finger print module is alone carried out with the help of the Arduino. X-bee module that is connected to both the Raspberry Pi and Arduino serves as a serial communication module. An indication of start, stop and the status of the student could also be represented with the help of the Raspberry Pi. This should be carried out in each and every day and the attendance of the student is stored in the database. Through the database the student could receive the progress through SMS with the help of GSM module that is connected with the Raspberry Pi. Through this system, a discipline and obedience among the entire students could be achieved. A block diagram that shows the connection of the Raspberry Pi and the Arduino is shown in the figure given below.
The different hardware components used in the fingerprint recognition are as follows:
1) Biometric sensors: Biometric is derived from a Greek word bio means life and metric means measure (Middendorff, 2010). This system has been used widely instead of lock patterns or security pin code which could be case sensitive. These are highly defined security system and are used for many purposes like face recognition, DNA, voice pattern etc (Chirchi, Dr.Waghmare, and Chirchi, 2011). The fingerprint recognition records the finger print of the individual and record in the form of arches, whorls and loops and the outline will be in the form of minutiae, edge and furrow. The fingerprint matching could be done by the following: 1) Minutiae- This takes the fingerprint and stores that in a set of plane of points and the matching is done between the template and the input signal. 2) Correlation- This is done with the comparison of two fingerprint images and the acquaintance pixel is estimated. 3) Ridges- This is the advanced method that advances minutiae which lags in their quality. This could capture images in the form of ridges.
System Initial Design: System Block Diagram
Based on the biometric method, the sensor could be adopted. There are several sensors such as optical sensors, thermal sensors, electric field sensors, capacitive sensors, 3D sensors etc. Optical sensors work with the help of the light (Nayak, 2008). Electric field sensors work with the small alternative electric pulses that are generated due to the contact of the finger surface. Polymer TFT sensors works by measuring the light that comes out from the polymer substrate when the finger is in contact. The base of the sensor and the finger will act as a capacitance in the capacitive sensor and the changes in the capacitance could be represented as a fingerprint. The optical sensors could be the most adaptive one that uses a LED and CCD camera. A glass prism is placed where the finger of the student should be place (Patil, Sharma and Gajbhiye, 2015). Human body normally consist of ridges (papillary line) and grooves in our finger. When the finger is in contact with the prism the ridges the surface where the grooves does not. A light is passed through the prism at a certain angle. The light that contacts the ridges will be reflected back on the other hand, the grooves takes the light and remain dark. The figure that represents a biometric sensor with its image is shown below
2) Raspberry pi: The raspberry pi is the open hardware that makes CPU, graphics, USB controller etc, to work on board. There are many models in the raspberry pi and at latest we use raspberry pi 3 (Shahab, Shafait and Dengel, 2011). This is the third generation module that has a credit card sized single board computer which advances the old Model B+ and Model B of the raspberry pi. The processor implemented in the Raspberry pi 3 Model B is ten times faster than the first generation raspberry model. They have advanced feature of Bluetooth and wireless LAN connection through which many networks could be connected.
3) GSM module: Global System for mobile communication serves as a cellular communication module that is an open source system (Epshtein, Ofek and Wexler, 2010). This serves as a communication between a computer and a mobile network. Initially we need a proper working SIM card, power pin connecter and a power supply. The most commonly used GSM module is SIM900A. The SIM card should be properly placed in the card holder and it should be locked. The antenna should be adjusted correctly (Yi and Tian, 2011). The transmitter of the GSM module should be connected to the receiver module of the Raspberry Pi and vice-versa. Both the ground terminal should be accurately connected.
Technical Requirements: Hardware components
4) Wifi Module: The wifi module could provide access to any microcontroller through our wifi network (Zhang and Kasturi, 2008). This is a small System on Chip with the integration of TCP/IP protocol. The module ESP8266 is already programmed with AT command set firmware, which helps us in serving the maximum progress of wifi network by simply connecting it to the arduino board (Ma, Wang and Xiao, 2010). This is the most cost-effective method that helps in gaining the maximum advantage. This on-chip has been designed to get into minimum PCB board area.
5) ZigBee Module: At the present scenario we have many communication devices, but none of them meet the requirement of the sensors and the control system communication rate. These modern devices have a high baud rate communication but these are of high cost and consumes high amount of energy (Nikolaou and Papamarkos, 2009). This could be wiped out by the wireless zigbee technology that consumes less power even in a minimum bandwidth. This best known wireless communication device is low in its cost and could serves as a useful device in embedded environment that helps in home automation, industrial application etc. This works in IEEE 802.15.4 standard. Zigbee communication module describes the standard of physical layer and Media Access Control (MAC) for handling many devices at low baud rate. The best Zigbee’s could activate at certain frequencies like 868 MHz, 928MHz and 2.4 GHz (Khan, Khan, Zaheer and Khan, 2010). This communication network could cover an area of about 100 meters with the 250 kbps data rate and could be fixed with the routers for the wide range of communication. This particular data rate could be the best adapted rate for the transmission of sensor data and automation control. Zigbee serves as an efficient communication device when compared to the wifi module and the Bluetooth module. The configuration varies between master to master and master to slave network with the conservation of battery.
6) LCD display: The Liquid Crystal Display screen works on the principle of blocking the light source. This consumes less power when compared to the LED’s. This is just an indication that shows the status of the working condition.
Arduino Software (IDE): It’s an open source device that consists of the microcontroller kit generally programmed with the dialect features of languages like c or c++. This has been widely used in various platforms by interfacing with the various digital platforms that can able to sense and control the objects in the physical environment. The product has been licensed under GNU Lesser General Public License (LGPL) that provides the permission in the manufacture of several arduino boards and their software allocation. The arduino consists of micro processors and controllers with the several digital and analog pins that could be interfaced with the other hardware components. The arduino could be interface through the personal computers with the help of the Universal Serial Bus (USB) cables (Roman, ALcaraz, Lopez & Sklavos, 2011). This promotes the Integrated Development Environment (IDE) platform instead of using the traditional one. This was developed in the year of 2003, which provides a user friendly environment for the automation and the automation systems.
Arduino Software (IDE) should be loaded with the program coding which is known as sketches. These programs are written in the text editor and saved with their file extensions. First the IDE verifies the sketches and it displays the error messages if the coding has some problem. Then it compiles the coding and then uploads it to the arduino board followed by the simulation. If we have to open a new project then click new icon. Menu bar includes File, Edit, Sketch, Tools and help from which the requirements could be done. Each and every time when we program the sketch, it should be saved before running the code.
schematic diagram for the implementation of attendance system using Fingerprint recognition. The fingerprint recognition could be carried out in three different stages. The scanning process could be carried out with the help of the sensors in the glass plate or prism and then the classification is done with the different minutiae that are noted for each student (Li, 2012). At last, the comparison is done between two fingerprints and checked whether they are identical or not.
The LCD is connected with the arduino port. Most probably we used arduino port of A0, A1, A2, A3, and A4. Then the Zigbee-1 communication module has transmitter and receiver port that is connected with the transmitter and receiver port of arduino module vice versa. In the same way the Raspberry Pi transmitter should be connected with the receiver port of the GSM module and vice versa. Another zigbee-2 communication module is connected with the Raspberry Pi by means of a serial connector. Several LED and switch board is connected with the Raspberry pi for the indication purpose.
First the power supply has to be provided for all the components required. We have to wait until the module configures and connects with the wifi module. Then we have to press the start button. The system waits until it receives the character. When it receives the character ‘E’ then it starts the enrollment process. It store the students fingerprint in the database. This is done for every student. Now when character ‘I’ is pressed then the system has to identify the fingerprint of the particular student. When it identifies the student fingerprint then it displays the student ID number. Else, it does not display anything. Then the attendance of that particular day will be send as a message with the help of the GSM module. After this process is done we have to press the stop button. The arduino sends the entire data to the Raspberry PI through the serial communication device. This is later stored as a text file in the database system for later reference.
The complete process of recognition of fingerprint includes:
Fingerprint Scanning: Sometimes scanning could be affected with the humidity, dryness and scars present in the skin. This could be neglected with the usage of a high defined sensor with the improved quality.
Improving Fingerprint image quality: By improving the quality of the image the ridges in the finger could be identified properly.
Image processing: This is the important process in the feature extraction and classification. In the classification, the process could be tedious due to some fingerprint that will not match certain requirement. This becomes difficult for classifying the images both by algorithm based technique as well as by man-made technique. Highly defined pattern classification system is used in the criminal investigation department for the identification of the fingerprints. After this process the fingerprint should be extracted that are done with the minutiae.
Verification: Here two features of the minutiae are compared that relies upon the quality of the image produced.
Initially the SIM card should be inserted into the card holder of the GSM module. Then it has been locked by pulling it to the opposite direction. The SIM card should maintain a particular balance and it should be checked properly before synchronizing the device with the module. The RF provided in the GSM should be adjusted and tightened after fixing it without rotating it. The pins of the GSM should be connected with the Raspberry PI as mentioned above. The modem should have a proper power supply, which is indication by a red LED. The blue LED starts blinking continously, indicating that the system is searching for the network. This takes around 10-60 seconds. When the system is set with the particular network, it starts to blink for every 3 seconds (Chen, 2012). The host system will set automatically to a baud rate of about 9600 and 115200. We should receive the response as “OK” by sending the character ‘A’ while synchronizing with the system before sending “AT”. When we receive this response then it means that the device and the GSM modem is properly coordinated. The design of Raspberry PI connected with the GSM module is shown in the figure given below.
The main benefit of this system is that it can avoid any devilish behavior of the student from attending regular classes. It could avoid the complexity and thus used in the company and organization for monitoring the presence of an employee. The data provided by this method could be completely safe and secure.
References:
Bhalla, V., Singla, T., Gahlot, A. and Gupta, V., (2013). Bluetooth Based Attendance Management System. International Journal of Innovations in Engineering and Technology (IJIET). 3(1).
Shafi, Q., Khan, J., Munir, N. and Baloch, N.K. (2010). Fingerprint Verification over the Network and its Application in Attendance Management in 2010 International Conference on Electronics and Information Engineering (ICEIE).
Ashraf, Q. M. and Habaebi, M. H. (2015). Autonomic schemes for threat mitigation in Internet of Things in Elsevier Journal of Network and Computer Applications, 49(1), pp. 112-127.
Middendorff, C. (2010) Multi-Biometric Approaches to Ear Biometrics and Soft Biometrics. A dissertation Submitted to the Graduate School of the University of Notre Dame
Chirchi E. Roselin Vanaja, Dr.Waghmare, L.M. and Chirchi, E.R., (2011) Iris Biometric Recognition for Person Identification in Security Systems in International Journal of Computer Applications. 24(9), June 2011.
Nayak, D.R. (2008). A Novel Architecture for Embedded Biometric Authentication System in 2008 Second UKSIM European Symposium on Computer Modeling and Simulation.
Patil, P., Sharma, S. and Gajbhiye, R. B. (2015) A Study- Impact of Internet of Things (IOT) For Providing Services for Smart City Development in International Journal of Advance Research in Computer Science and Management Studies, 3(6).
Shahab, A., Shafait, F. and Dengel, A. (2011). ICDAR 2011 robust reading competition: ICDAR Robust Reading Competition Challenge 2: Reading text in scene images in Proc. Int. Conf. Document Anal. Recognition, pp. 1491–1496.
Advance Data Reports from the National Health Interview Survey (2008). [Online]. Available: https://www.cdc.gov/nchs/nhis/nhis_ad.htm.
Epshtein, B., Ofek, E. and Wexler, Y. (2010). Detecting text in natural scenes with stroke width transform,” in Proc. Comput. Vision Pattern Recognition, pp. 2963–2970.
Yi, C. and Tian, Y., (2011). Assistive text reading from complex background for blind persons in Proc. Int. Workshop Camera-Based Document Anal.Recognit. vol. LNCS-7139, pp. 15–28.
Yi, C. and Tian, Y. (2011). Text string detection from natural scenes by structure based partition and grouping in IEEE Trans. Image Process. vol. 20(9), pp. 2594–2605
International Workshop on Camera-Based Document Analysis and Recognition (CBDAR 2005, 2007, 2009, 2011). [Online]. Available: https://www.m.cs.osakafuu.ac.jp/cbdar2011/
Zhang, J. and Kasturi, R. (2008). Extraction of text objects in video documents: recent progress in Proc. IAPR Workshop Document Anal. Syst. pp. 5–17.57
Ma, L., Wang, C. and Xiao, B. (2010). Text detection in natural images based on multi-scale edge detection and classification in Proc. Int. Congr.Image Signal Process. vol.4, pp. 1961–1965.
Nikolaou, N. and Papamarkos, N. (2009). Color reduction for complex document images in International Journal Imaging System Technol. vol. 19, pp. 14–26, 2009.
Khan, R., Khan, S. U., Zaheer, R. and Khan, S. (2010). Future Internet: The Internet of Things Architecture, Possible Applications and Key Challenges. Proceedings of Frontiers of Information Technology (FIT). pp. 257-260.
Roman, R., ALcaraz, C., Lopez, J. & Sklavos, N. (2011). Key Management Systems for Sensor Networks in the Context of the Internet of Things. Computers & Electrical Engineering. 37(2), pp. 147-159
Li, L (2012). Study of Security Architecture in the Internet of Things. Measurement, Information and Control (MIC). Vol 1, pp. 374-377
Chen, W.(2012). AN IBE BASED SECURITY SCHEME OF INTERNET OF THINGS. Cloud Computing and Intelligent Systems (CCIS). pp. 1046, 1049
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