Comparative Analysis for A Real Time Face Recognition System Using Raspberry Pi1croreprojects@gmail.com
Currently, industries, organisations are using personal identification strategies such as RFID, Iris recognition, Fingerprint identification is used for taking attendance. Among of all these personal identification strategies including face recognition is most natural, less time is taken and highly efficient one despite being difficult to implement, a continuous observation for overcoming it. It has several applications in attendance management systems and security systems. In this work, a system is implemented that takes attendance for students during lecture, employees in industries and etc. using face detection and recognition technology. A time period is set for taking attendance and the database is automatically uploaded into the web server through the internet connectivity. This process is done without any human intervention. In the system a Raspberry Pi installed with OpenCV library and a Raspberry Pi Camera module is connected for facial detection and Recognition. The data is stored in the memory card connected to Raspberry Pi and it can be accessed through the internet. The results show that a continuous observation increases accuracy and maximizes the output.
The current systems that are used for updating attendance automatically are usually RFID based, Bio-metric based and MATLAB based.
Usually, the manual method of taking attendance is difficult and a time-consuming process.
Hence it is important to construct an efficient method for managing attendance automatically.
Another advantage of these types is that inclusion of fake attendance can be prevented. Open Command Visualization (Open-CV) is an open source library in which the source code is open and it is useful in visual field such as image processing.
The main motto of this work is to take and manage attendance using face recognition.
Time Consuming Process.
Easily add Fake account.
The proposed system is used for taking attendance by using face recognition and managing the attendance in suitable environments such as colleges and offices.
Raspberry Pi Camera Module V2 attached to Raspberry Pi3 and it is placed where the people enter the office. Camera Module is used to capture video from.
which images of human faces is extracted? Then face recognition takes place and it automatically verifies with the existing database through library files present in OpenCV.
Face Recognition is generally more advanced and efficient than other systems.
By using Raspberry Pi, the system becomes scalable and flexible.
The system can be modified easily without disturbing the other components in the system.
As we will use Raspberry Pi to develop the system, the total system has become low power system.
New embedded technologies can be easily inserted into this development, due to the use of raspberry pi.
New connections like cascade connections, parallel connection, series connection to extend the system.
There are many algorithms available for Face detection and face recognition, the algorithm used in proposed system for detection is Haarcascade algorithm and for face recognition LBPH (Local Binary Pattern Histograms), Fisher faces and Eigen faces algorithm.
The open source library packages OpenCV 3.1.0, contains built-in face recognition algorithms has been utilized in the proposed system to study the comparison of different algorithms performance.
The proposed system uses Raspberry pi and Python language for the hardware implementation. Face recognition process always have speed and accuracy issues its being done on many other languages like C++, Java, MATLAB etc. The reason for selecting Python Language, runs faster than other languages and it is easier to code in Python on the Raspberry Pi. Due to the usage of Raspberry Pi the dependency on the underlying hardware platform (processor, ram and OS) is eliminated. The output from web cam is directly read in by Raspberry Pi which detect and recognized the image and the result is generated which will be shown on the computer.
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2.Quan-Xi, Li Gang M. 2012. An Efficient Automatic Attendance System Using Fingerprint Reconstruction Technique IJCSIS International Journal of Computer Science and Information Security.
3.U. Eze peter; Owerri Nigeria; C.K.A. JoeUzuegbu; Laz Uzoechi; F.K. 2013. Opera biometric based attendance system with remote real time monitoring tertary institutions in developing countries emerging and sustainable technologies for power and ICT in a developing society NIGERCON, IEEE.
4.Design and implementation of iris based attendance management system Electrical Engineering and Information Communication Technology (ICEEICT), 2015 International Conference.
5.Adam Schmidt, Andrzej Kasinski, “The Performance of the Haar Cascade Classifiers Applied to the Face and Eyes Detection”, Computer Recognition Systems 2016.
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