IRJET- Intelligent Automated Attendance System based on Facial Recognition
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This document presents a proposed intelligent automated attendance system based on facial recognition. The system aims to automate the attendance marking process in educational institutions to make it faster and less error-prone compared to manual methods. It works by using computer vision techniques like haar cascade classification for face detection and local binary pattern histograms for face recognition. The system architecture involves capturing images, detecting faces, recognizing students by matching faces to a training database, and marking the attendance automatically. Algorithms like haar cascade and local binary patterns are used for face detection and recognition. The proposed system aims to solve issues with existing manual and automated attendance systems like time consumption, errors, and lack of accuracy.
![International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 06 Issue: 04 | Apr 2019 www.irjet.net p-ISSN: 2395-0072
© 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 3650
Intelligent Automated Attendance System Based on Facial Recognition
Ajimi . S1, Shammy Arun Mathew2, Dr. Dinakar Das C.N3
1P.G. Scholar, Applied Electronics & Instrumentation, Lourdes Matha College of Science & Technology, Kerala, India
2Assistant professor, Dept of ECE, Lourdes Matha College of Science & Technology, Kerala, India
3Associate Professor, Dept. of ECE, Lourdes Matha College of Science &Technology, Kerala, India
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Abstract - In School and colleges, daily attendance is a
common and important activity to check the performance of
the student. For large number of students, manualattendance
will be a difficult process. In these days, for overcome these
difficulties, some of the automated system developed. Each
these automated system have some drawbacks like cost, fake
attendance, low accuracy and intrusiveness. For solve these
drawbacks, need to develop a smart and automated
attendance system. This paper introduces face detection
method using the haar cascade classifier algorithm and
recognition using local binary pattern histogram algorithm
technique. The system will record the attendance of the
students in class room environment.
Key Words: Face Detection, Face Recognition, Haar cascade
classifier, LBPH algorithm
1. INTRODUCTION
Attendance is a very important factor in institutions and
organizations to maintain the record of lectures, salary, and
work hours etc. Manual method of attendance is followed in
most of the institutions and organizations using papers and
files. Now days, some of them are shifted to biometric
technique. The current method in colleges and schools used,
that are professors adding the attendance in a paper sheet
with the roll number and name of the studentandafter mark
of attendance, professors updates the attendance in a excel
sheet themselves. This type of method is more time
consuming and hectic. The question is why not shifting toan
automated attendance system which works on face
recognition technique. At the entry of classroom, have to
mark the attendance of the students and employees.
2. RELATED WORK
Smart attendance monitoring system: A face recognition
based attendance system for classroom environment[1]this
system using face recognition and face quality assessment,
help the attendance marking more easier. Once the system is
trained then it can be used in futurewithoutanyerror.Ituses
graphical user interface. The names are stored in ascending
order by this way, it reduce the efforts in conventional
process. The names are roll down based on alphabets.
Implementation of classroom attendance system based on
face recognition in class [2]this system capturing images of
individuals andstored the detected image of faceisdatabase
by using viola and Jones algorithm. Comparison is done by
eigen face method. And authorised person can mark
attendance by just opening an exe file.
Fingerprint attendance system for classroom needs [3]
makes the process easier and faster by using graphical user
interface. This is achieved by firstly registering the finger
print of students and that are stored in databases. Since
fingerprints are unique in nature, so each student can mark
this own attendance easily and it reduce time consumption
related to manual method.
RFID based attendance system [4] that can identify each
person by providing a ID card with an RFID tag make the
process, reduce the problem that are related to the
conventional method. This is done by placing ID card on the
reader and attendance marked at the right time without any
time delay.
A design and implementation of a wireless iris recognition
attendance management system [5] The Daugman’s
algorithm describe a system for marks attendance using iris
recognition. For making this process easierthesystemisfirst
trained and images are stored in database. Attendance is
marked by matching this image.
3. PROPOSED MODEL
3.1 System Architecture
The system architectureconsist of camera, detecting face,
face recognition, training set, storing in database and finally
marked the attendance. This system works using the Python
programminglanguage.Theproposedautomatedattendance
system based on face recognition.
Theproposedblockdiagramofautomatedattendancesystem
is shown in the Figure 1.
Fig -1: Proposed block diagram](https://image.slidesharecdn.com/irjet-v6i41156-190729061716/85/IRJET-Intelligent-Automated-Attendance-System-based-on-Facial-Recognition-1-320.jpg)
![International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 06 Issue: 04 | Apr 2019 www.irjet.net p-ISSN: 2395-0072
© 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 3652
This system contain first image is captured and convert into
the gray scale image. That means colored image is converted
to gray scale image. Then face is detect and recognition the
face. And it is matched with the trained ones. If it is matched
with the trained one the attendance is marked as update the
attendance and generate report.
6. CONCLUSION AND FUTURE SCOPE
This replaces the manual attendance with the automated
system, which will time saving, low cost, efficient and fast.
The whole system is implemented in PYTHON programming
language. Haar cascade classifiers and Local Binary Pattern
Histogram are used in this system implementation, which
gives better accurate results than other method for
implementing the system.
In future works for the students gets SMS regarding his/her
attendance as alert. This purpose is done used by a GSM
module. Also parents get the SMS alert of their student
attendance.
REFERENCES
[1] Shubhobrata Bhattacharya, Gowtham Sandeep Nainala,
Prosenjit Das and Aurobinda Routray, “Smart
Attendance Monitoring System: A Face Recognition
based Attendance System for Classroom Environment”,
2018 IEEE 18th International Conference on Advanced
Learning Technologies, pp.358-360, 2018.
[2] Ajinkya Patil, Mrudang Shukla, “Implementation of
Classroom Attendance System Based On Face
Recognition in Class”, International Journal of Advances
in Engineering & Technology. ISSN: 22311963, Vol. 7,
Issue 3, pp. 974-979, July, 2014.
[3] B. K. Mohamed and C. Raghu “Fingerprint attendance
system for classroom needs”, in India Conference
(INDICON), 978-1-4673-2272-0/12/$31.00 ©2012
IEEE, pp.433-438.
[4] T. Lim, S. Sim, and M. Mansor “RFID based attendance
system”, in Industrial Electronics Applications, 2009.
ISIEA 2009. IEEE Symposium on, vol. 2, pp. 778-782,
IEEE, 2009.
[5] S. Kadry and K. Smaili, “A design and implementation of
a wireless iris recognition attendance management
system”, Information Technology and control, vol. 36,
no. 3, pp. 323–329, 2007.
[6] Preeti Mehta, Dr. Pankaj Tomar, "An Efficient
Attendance Management System based on Face
Recognition using Matlab and Raspberry Pi 2", May
2016 International Journal of Engineering Technology
Science and Research (IJETSR) ISSN 2394 – 3386
Volume 3, Issue 5.
[7] Ashish Choudhary, Abhishek Tripathi, Abhishek Bajaj,
Mudit Rathi, andB.MNandini,"AttendanceSystemUsing
Face Recognition", International Journal of Modern
Trends in Engineering and Research (IJMTER) Volume
03, Issue 04, ISSN (Online):2349–9745; ISSN
(Print):2393-8161, April– 2016.
[8] Michihiko Minon, Weijane Cin, Tetsuo Shoji, “Face
Recognition Based Lecture Attendance System” IEEE
Transaction June 2013.
[9] Shireesha Chintalapati, M.V. Raghunadh, “Automated
Attendance Management System Based on Face
Recognition Algorithms”, IEEEInternational Conference
on Computational IntelligenceandComputingResearch,
Jan 2014.
[10] Mashhood Sajid, Rubab Hussain, Muhammad
Usman, “A Conceptual Model for AutomatedAttendance
Marking System Using Facial Recognition”, IEEE
Conference Publication,978-1-4799-5421-6/14/$31.00
© 2014 IEEE, pp. 07-10.](https://image.slidesharecdn.com/irjet-v6i41156-190729061716/85/IRJET-Intelligent-Automated-Attendance-System-based-on-Facial-Recognition-3-320.jpg)
IRJET- Intelligent Automated Attendance System based on Facial Recognition
- 1. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 04 | Apr 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 3650 Intelligent Automated Attendance System Based on Facial Recognition Ajimi . S1, Shammy Arun Mathew2, Dr. Dinakar Das C.N3 1P.G. Scholar, Applied Electronics & Instrumentation, Lourdes Matha College of Science & Technology, Kerala, India 2Assistant professor, Dept of ECE, Lourdes Matha College of Science & Technology, Kerala, India 3Associate Professor, Dept. of ECE, Lourdes Matha College of Science &Technology, Kerala, India ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - In School and colleges, daily attendance is a common and important activity to check the performance of the student. For large number of students, manualattendance will be a difficult process. In these days, for overcome these difficulties, some of the automated system developed. Each these automated system have some drawbacks like cost, fake attendance, low accuracy and intrusiveness. For solve these drawbacks, need to develop a smart and automated attendance system. This paper introduces face detection method using the haar cascade classifier algorithm and recognition using local binary pattern histogram algorithm technique. The system will record the attendance of the students in class room environment. Key Words: Face Detection, Face Recognition, Haar cascade classifier, LBPH algorithm 1. INTRODUCTION Attendance is a very important factor in institutions and organizations to maintain the record of lectures, salary, and work hours etc. Manual method of attendance is followed in most of the institutions and organizations using papers and files. Now days, some of them are shifted to biometric technique. The current method in colleges and schools used, that are professors adding the attendance in a paper sheet with the roll number and name of the studentandafter mark of attendance, professors updates the attendance in a excel sheet themselves. This type of method is more time consuming and hectic. The question is why not shifting toan automated attendance system which works on face recognition technique. At the entry of classroom, have to mark the attendance of the students and employees. 2. RELATED WORK Smart attendance monitoring system: A face recognition based attendance system for classroom environment[1]this system using face recognition and face quality assessment, help the attendance marking more easier. Once the system is trained then it can be used in futurewithoutanyerror.Ituses graphical user interface. The names are stored in ascending order by this way, it reduce the efforts in conventional process. The names are roll down based on alphabets. Implementation of classroom attendance system based on face recognition in class [2]this system capturing images of individuals andstored the detected image of faceisdatabase by using viola and Jones algorithm. Comparison is done by eigen face method. And authorised person can mark attendance by just opening an exe file. Fingerprint attendance system for classroom needs [3] makes the process easier and faster by using graphical user interface. This is achieved by firstly registering the finger print of students and that are stored in databases. Since fingerprints are unique in nature, so each student can mark this own attendance easily and it reduce time consumption related to manual method. RFID based attendance system [4] that can identify each person by providing a ID card with an RFID tag make the process, reduce the problem that are related to the conventional method. This is done by placing ID card on the reader and attendance marked at the right time without any time delay. A design and implementation of a wireless iris recognition attendance management system [5] The Daugman’s algorithm describe a system for marks attendance using iris recognition. For making this process easierthesystemisfirst trained and images are stored in database. Attendance is marked by matching this image. 3. PROPOSED MODEL 3.1 System Architecture The system architectureconsist of camera, detecting face, face recognition, training set, storing in database and finally marked the attendance. This system works using the Python programminglanguage.Theproposedautomatedattendance system based on face recognition. Theproposedblockdiagramofautomatedattendancesystem is shown in the Figure 1. Fig -1: Proposed block diagram
- 2. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 04 | Apr 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 3651 3.1.1 Capture Image The system contain camera that capture the faces of students. Then the image is sending for face detection. 3.1.2 Face Detection Haar cascadeclassifier used to face detection. Face detection means identify and locating a face in a captured image. 3.1.3 Image Pre processing The system first images captured and convert it into gray scale image. That means colored image is converting to gray scale image. 3.1.4 Training set The recognized the image is detected facesfromtraineddata. 3.1.5 Face recognition The system important part is face recognition. The detected image is done by cropped and processed later by face recognition. This method is used of identify the each person using his face image. 3.1.6 Attendance marked After recognition process if the particular student will be marked the attendance in the database of students details. The attendance status of student can also be identified with the help of user id and password. 4. PROPOSED ALGORITHM This system important part is face detection and face recognition. For face detection using haar cascade classifier face detection algorithmwhileforfacerecognitionusinglocal binary pattern histogram algorithm. 4.1 Face Detection using Haar Cascade Classifier Paul Viola and Michael Jones proposed an effective object detectionmethodofobjectdetectionusinghaarfeaturebased cascadeclassifier. This approach based on machine learning. From a lot of positive and negative images starts training for the cascade function. For object detection this functions will be used. For face detection, we need some positive and negative images, which have no face images, for train the classifier. After the training, extracts the features from it. Haar feature functions are our conventional kernal. From under white rectangle from sum of pixels under black rectangle, each feature is a single value obtained by subtracting sum of pixels. 4.2 Face Recognizer using Local Binary Pattern Histogram In locally dataset, characterize each image and performs analyzes when new unknown image is provided. In the dataset, compares the result to each of dataset the images. By characterizing the local patternswithinthelocationin the image, analyzing the images. This method is much better than other methods. For given inputimage,LBPHalgorithms generate new histogram during recognition process. Then compare with the early stored histograms. It will detect the top best matched histogram it will be return with the associated label with the corresponding histogram. In this system, there are 3 steps for the face recognition. First is, with the label for each person’s images which is get from input. From each image detects the faces. Then assign each detected face with the associated label to the person who belongs to. The second step is, by feeding data from stepfirstto train Open CV’s LBPH face recognizer. The third step is, to face recognizer, transfers some test images. And tests if it predicts them correctly. 5. SYSTEM FLOW CHART The flowcharts the operation of automated attendance system shown in figure 2. Fig -2: System flow diagram
- 3. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 04 | Apr 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 3652 This system contain first image is captured and convert into the gray scale image. That means colored image is converted to gray scale image. Then face is detect and recognition the face. And it is matched with the trained ones. If it is matched with the trained one the attendance is marked as update the attendance and generate report. 6. CONCLUSION AND FUTURE SCOPE This replaces the manual attendance with the automated system, which will time saving, low cost, efficient and fast. The whole system is implemented in PYTHON programming language. Haar cascade classifiers and Local Binary Pattern Histogram are used in this system implementation, which gives better accurate results than other method for implementing the system. In future works for the students gets SMS regarding his/her attendance as alert. This purpose is done used by a GSM module. Also parents get the SMS alert of their student attendance. REFERENCES [1] Shubhobrata Bhattacharya, Gowtham Sandeep Nainala, Prosenjit Das and Aurobinda Routray, “Smart Attendance Monitoring System: A Face Recognition based Attendance System for Classroom Environment”, 2018 IEEE 18th International Conference on Advanced Learning Technologies, pp.358-360, 2018. [2] Ajinkya Patil, Mrudang Shukla, “Implementation of Classroom Attendance System Based On Face Recognition in Class”, International Journal of Advances in Engineering & Technology. ISSN: 22311963, Vol. 7, Issue 3, pp. 974-979, July, 2014. [3] B. K. Mohamed and C. Raghu “Fingerprint attendance system for classroom needs”, in India Conference (INDICON), 978-1-4673-2272-0/12/$31.00 ©2012 IEEE, pp.433-438. [4] T. Lim, S. Sim, and M. Mansor “RFID based attendance system”, in Industrial Electronics Applications, 2009. ISIEA 2009. IEEE Symposium on, vol. 2, pp. 778-782, IEEE, 2009. [5] S. Kadry and K. Smaili, “A design and implementation of a wireless iris recognition attendance management system”, Information Technology and control, vol. 36, no. 3, pp. 323–329, 2007. [6] Preeti Mehta, Dr. Pankaj Tomar, "An Efficient Attendance Management System based on Face Recognition using Matlab and Raspberry Pi 2", May 2016 International Journal of Engineering Technology Science and Research (IJETSR) ISSN 2394 – 3386 Volume 3, Issue 5. [7] Ashish Choudhary, Abhishek Tripathi, Abhishek Bajaj, Mudit Rathi, andB.MNandini,"AttendanceSystemUsing Face Recognition", International Journal of Modern Trends in Engineering and Research (IJMTER) Volume 03, Issue 04, ISSN (Online):2349–9745; ISSN (Print):2393-8161, April– 2016. [8] Michihiko Minon, Weijane Cin, Tetsuo Shoji, “Face Recognition Based Lecture Attendance System” IEEE Transaction June 2013. [9] Shireesha Chintalapati, M.V. Raghunadh, “Automated Attendance Management System Based on Face Recognition Algorithms”, IEEEInternational Conference on Computational IntelligenceandComputingResearch, Jan 2014. [10] Mashhood Sajid, Rubab Hussain, Muhammad Usman, “A Conceptual Model for AutomatedAttendance Marking System Using Facial Recognition”, IEEE Conference Publication,978-1-4799-5421-6/14/$31.00 © 2014 IEEE, pp. 07-10.