IRJET- A Study on Automated Attendance System using Facial Recognition
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The document discusses an automated attendance system using facial recognition. It begins with an introduction to facial recognition and the motivation for developing an automated attendance system. It then reviews previous work on facial recognition algorithms such as PCA, Viola-Jones, and neural networks. The proposed system is described as using SVM on LBP features for facial recognition due to its high accuracy. Key advantages of the proposed system include being cost-efficient, easy to deploy, and preventing time fraud. The document concludes facial recognition can effectively automate attendance tracking in educational or commercial organizations.
![International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 06 Issue: 12 | Dec 2019 www.irjet.net p-ISSN: 2395-0072
© 2019, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 512
A Study on Automated Attendance System using Facial Recognition
Ms. Munmun Bhagat[1], Chaitanya Kirkase[2], Yash Hawaldar[3], Aishwarya Paigude[4], Shivani Nimbalkar[5]
1(Asst. Professor, Dept. of Computer Engineering RMD Sinhgad School of Engineering Pune, Maharashtra, India)
2,3,4,5(Students, Dept. of Computer Engineering RMD Sinhgad School of Engineering Pune, Maharashtra, India)
----------------------------------------------------------------------------------***------------------------------------------------------------------------------
Abstract - Attendance marking system has been become a challenging task in the real-time system. It is tough to mark the
attendance of the candidate in the huge classroom/hall, and there are many students attend the class. Many attendance
management systems have been implemented in the current research. However, the attendance management system by using
facial recognition still has issues which allow the research to improve the current research to make the attendance
management system working well. The paper has conducted a literature survey on the previous work by different researcher
has done on their research paper.
Keywords – Automated, Face Detection, Face Recognition, Algorithms, Correlation, Attendance.
1. INTRODUCTION
FACE Recognition has received many interests in recent years of face recognition development and has become a popular
research. Moreover, it is a critical application in image analysis, and it is a very challenge to create an automated system
based on face recognition; which has an ability to recognize human face accuracy. Solving the manual attendance problem
and time-consuming, much research has been conducted with the automated or smart attendance management system to
resolve the issues of manual attendance.
The key motivation is to go for this project was the slow down the inefficient traditional manual attendance system. This
made us to think why not make it automated fast and mush efficient. Also some face detection and recognition techniques
are in use by department like crime investigation where they use CCTV footages and face detection and recognition.
2. LITERATURE REVIEW
Sr No. Author Algorithm Problem Summary
1. Visar Shehu [1] PCA The recognition rate is
56%, having a problem to
recognize student in year 3
or 4
Using HAAR Classifier and
computer vision algorithm to
implement face recognition.
2. Viola, M. J. Jones
[8]
Viola and Jones
algorithm
In Viola and Jones the
result depends on the
data and weak
classifiers. The quality
of the final detection
depends highly on the
consistence of the
training set. Both the
size of the sets and the
interclass variability
are important factors
to take in account.
The analysis shows
very bad results when
in case of multiple
person with different
sequence.
The training of the data
should be done in correct
manner so that the quality
final detection will
increase.
System overview should
contain the overall
architecture that will give
the clear and comprehensive
information of the project.
3. Kasar, M.,
Bhattacharyya, D.
and Kim, T. [9]
Neural-Network Detection process is
slow and computation
is complex.
Overall performance is
weaker than Viola-
Jones algorithm.
Accurate only if large size of
image was trained.](https://image.slidesharecdn.com/irjet-v6i1259-200104095046/85/IRJET-A-Study-on-Automated-Attendance-System-using-Facial-Recognition-1-320.jpg)
![International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 06 Issue: 12 | Dec 2019 www.irjet.net p-ISSN: 2395-0072
© 2019, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 513
4. Pratiksha M. Patel
[10]
Contrast Limited
Adaptive Histogram
Equalization (CLAHE)
More sensitive to noise
compared to histogram
equalization.
Unlike, HE which works on
entire image, it works on
small data regions. Each tile's
contrast is enhanced to
ensure uniformly distributed
histogram. Bilinear
interpolation is then used to
merge the neighboring tiles.
Advantage:-
It prevent over enhancement
as well as noise amplification.
5. Suman Kumar
Bhattacharyya &
Kumar Rahul.
[6]
Fisher face/ LDA
(Linear Discriminant
Analysis )
Bigger database is
required because images
of different expression of
the individual have to be
trained in same class.
It depend more on
database compared to
PCA.
Images of individual with
different illumination, facial
expressions able to be
recognized if more samples
are trained.
6. Varsha Gupta,
Dipesh Sharma
[7]
Successive mean
quantization
transform (SMQT)
Features and sparse
network of winnows
(SNOW) Classifier
Method.
The region contain very
similar to grey value
regions will be
misidentified as face.
1. Capable to deal with
lighting problem in object
detection.
2. Efficient in computation
7.
Syen navaz
[2]
PCA, ANN
Low accuracy with the
big size of images to
train with PCA.
Hight Computational
cost due to combining
PCA and ANN
Using PCA to train and
reduce dimensionality
and ANN to classify input
data and find the pattern.
Using PCA and ANN to do
a better attendance result.
8. Md. Abdur Rahim
et al
[11]
LBP(Local Binary
Pattern)
Training time is longer
than PCA and LDA.
It is able to overcome
variety of facial
expressions, varying
illumination, image
rotation and aging of
person.
Accuracy till 90.45%
3. PROPOSED SYSTEM
By considering the disadvantages of some systems mentioned in above table the attempt is made to implement the
automated facial attendance system using SVM on LBP feature as LBP algorithm gives good accuracy as compare to other
systems respectively.
The proposed system introduces an automated attendance system which integrates an Android app and face recognition
algorithms. Any device with a camera can capture an image or a video and upload to the server using web app. The
received file undergoes face detection and face recognition so the detected faces are extracted from the image.
Step by Step Description:-
Step one: User uploads a video / grabs images using camera on From Android mobile and send it to application server
Step Two: Once we get the faces apply the preprocessing on images like noise removal, normalization etc.
Step Three: Convert the image into Gray scale by taking the average of the each pixel RGB.
Step Four: Apply SVM on Local Binary Patterns Histograms features.
Step Five: Mark and Manage the Attendance.](https://image.slidesharecdn.com/irjet-v6i1259-200104095046/85/IRJET-A-Study-on-Automated-Attendance-System-using-Facial-Recognition-2-320.jpg)
IRJET- A Study on Automated Attendance System using Facial Recognition
- 1. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 12 | Dec 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 512 A Study on Automated Attendance System using Facial Recognition Ms. Munmun Bhagat[1], Chaitanya Kirkase[2], Yash Hawaldar[3], Aishwarya Paigude[4], Shivani Nimbalkar[5] 1(Asst. Professor, Dept. of Computer Engineering RMD Sinhgad School of Engineering Pune, Maharashtra, India) 2,3,4,5(Students, Dept. of Computer Engineering RMD Sinhgad School of Engineering Pune, Maharashtra, India) ----------------------------------------------------------------------------------***------------------------------------------------------------------------------ Abstract - Attendance marking system has been become a challenging task in the real-time system. It is tough to mark the attendance of the candidate in the huge classroom/hall, and there are many students attend the class. Many attendance management systems have been implemented in the current research. However, the attendance management system by using facial recognition still has issues which allow the research to improve the current research to make the attendance management system working well. The paper has conducted a literature survey on the previous work by different researcher has done on their research paper. Keywords – Automated, Face Detection, Face Recognition, Algorithms, Correlation, Attendance. 1. INTRODUCTION FACE Recognition has received many interests in recent years of face recognition development and has become a popular research. Moreover, it is a critical application in image analysis, and it is a very challenge to create an automated system based on face recognition; which has an ability to recognize human face accuracy. Solving the manual attendance problem and time-consuming, much research has been conducted with the automated or smart attendance management system to resolve the issues of manual attendance. The key motivation is to go for this project was the slow down the inefficient traditional manual attendance system. This made us to think why not make it automated fast and mush efficient. Also some face detection and recognition techniques are in use by department like crime investigation where they use CCTV footages and face detection and recognition. 2. LITERATURE REVIEW Sr No. Author Algorithm Problem Summary 1. Visar Shehu [1] PCA The recognition rate is 56%, having a problem to recognize student in year 3 or 4 Using HAAR Classifier and computer vision algorithm to implement face recognition. 2. Viola, M. J. Jones [8] Viola and Jones algorithm In Viola and Jones the result depends on the data and weak classifiers. The quality of the final detection depends highly on the consistence of the training set. Both the size of the sets and the interclass variability are important factors to take in account. The analysis shows very bad results when in case of multiple person with different sequence. The training of the data should be done in correct manner so that the quality final detection will increase. System overview should contain the overall architecture that will give the clear and comprehensive information of the project. 3. Kasar, M., Bhattacharyya, D. and Kim, T. [9] Neural-Network Detection process is slow and computation is complex. Overall performance is weaker than Viola- Jones algorithm. Accurate only if large size of image was trained.
- 2. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 12 | Dec 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 513 4. Pratiksha M. Patel [10] Contrast Limited Adaptive Histogram Equalization (CLAHE) More sensitive to noise compared to histogram equalization. Unlike, HE which works on entire image, it works on small data regions. Each tile's contrast is enhanced to ensure uniformly distributed histogram. Bilinear interpolation is then used to merge the neighboring tiles. Advantage:- It prevent over enhancement as well as noise amplification. 5. Suman Kumar Bhattacharyya & Kumar Rahul. [6] Fisher face/ LDA (Linear Discriminant Analysis ) Bigger database is required because images of different expression of the individual have to be trained in same class. It depend more on database compared to PCA. Images of individual with different illumination, facial expressions able to be recognized if more samples are trained. 6. Varsha Gupta, Dipesh Sharma [7] Successive mean quantization transform (SMQT) Features and sparse network of winnows (SNOW) Classifier Method. The region contain very similar to grey value regions will be misidentified as face. 1. Capable to deal with lighting problem in object detection. 2. Efficient in computation 7. Syen navaz [2] PCA, ANN Low accuracy with the big size of images to train with PCA. Hight Computational cost due to combining PCA and ANN Using PCA to train and reduce dimensionality and ANN to classify input data and find the pattern. Using PCA and ANN to do a better attendance result. 8. Md. Abdur Rahim et al [11] LBP(Local Binary Pattern) Training time is longer than PCA and LDA. It is able to overcome variety of facial expressions, varying illumination, image rotation and aging of person. Accuracy till 90.45% 3. PROPOSED SYSTEM By considering the disadvantages of some systems mentioned in above table the attempt is made to implement the automated facial attendance system using SVM on LBP feature as LBP algorithm gives good accuracy as compare to other systems respectively. The proposed system introduces an automated attendance system which integrates an Android app and face recognition algorithms. Any device with a camera can capture an image or a video and upload to the server using web app. The received file undergoes face detection and face recognition so the detected faces are extracted from the image. Step by Step Description:- Step one: User uploads a video / grabs images using camera on From Android mobile and send it to application server Step Two: Once we get the faces apply the preprocessing on images like noise removal, normalization etc. Step Three: Convert the image into Gray scale by taking the average of the each pixel RGB. Step Four: Apply SVM on Local Binary Patterns Histograms features. Step Five: Mark and Manage the Attendance.
- 3. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 12 | Dec 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 514 Figure: - System Architecture Competitive Advantages of Proposed System:- Currently either manual or biometric attendance systems are being used in which manual is hectic and time consuming. The biometric serves one at a time, so there is a need of such system which could automatically mark the attendance of many persons at the same time. This proposed system is cost efficient, no extra hardware required just a daily mobile or tablet, etc. Hence it is easily deployable. Not only in institutes or organizations, it can also be used at any public places or entry-exit gates for advance surveillance. One of the big benefits of using facial attendance systems in any organization is that you won’t have to worry about time fraud. 4. CONCLUSIONS In order to maintain the attendance this system has been proposed. It replaces the manual system with an automated system which is fast, efficient, cost and time saving as replaces the stationary material and the paper work. However the proposed system is expected to give desired results. Also the efficiency could be improved by integrating other efficient techniques. Here we discussed various method used by the researcher for face detection that can be used for educational or commercial organizations for monitoring student's attendance in a lecture by detecting the faces of the student. So in the next step, we are trying to build up a system with Improved Support Vector Machines (IVSM) on LBP features for the classification of the faces. REFERENCES 1) V. Shehu and A. Dika, “Using real time computer vision algorithms in automatic attendance management systems,” Inf. Technol. Interfaces (ITI), 2010 32nd Int. Conf., pp. 397–402, 2010. 2) A. S. S. NAVAZ and T. D. S. P. MAZUMDER, “Face Recognition using Principal Component Analysis and Neural Networks,” vol. 1, no. April, pp. 91–94, 2001.
- 4. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 12 | Dec 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 515 3) N. Kar, M. K. Debbarma, A. Saha, and D. R. Pal, “Study of Implementing Automated Attendance System Using Face Recognition Technique,” Int. J. Comput. Commun. Eng., vol. 1, no. 2, pp. 100–103, 2012. 4) J. Joseph and K. P. Zacharia, “Automatic Attendance Management System Using Face Recognition,” Int. J. Sci. Res., vol. 2, no. 11, pp. 327–330, 2013. 5) Kwok-Wai Wong, Kin-Man Lam, Wan-Chi Siu, An efficient algorithm for human face detection and facial feature extraction under different conditions, The Journal Of the Pattern Recognition Society, 25 Aug-2000. 6) Suman Kumar Bhattacharyya & Kumar Rahul. (2013), “Face Recognition by Linear Discriminant Analysis”, International Journal of Communication Network Security, V2(2), pp 31-35. (LDA) 7) Varsha Gupta, Dipesh Sharma. (2014), “A Study of Various Face Detection Methods”, International Journal of Advanced Research in Computer and Communication Engineering), vol.3, no. 5. 8) Viola, M. J. Jones. (2004), “Robust Real-Time Face Detection”, International Journal of Computer Vision 57(2), 137– 154 9) Pratiksha M. Patel (2016). Contrast Enhancement of Images and videos using Histogram Equalization. International Journal on Recent and Innovation Trends in Computing and Communication.V4 (11). 10) Kasar, M., Bhattacharyya, D. and Kim, T. (2016). Face Recognition Using Neural Network: A Review. International Journal of Security and Its Applications, 10(3), pp.81-100 11) Md. Abdur Rahim (2013), Face Recognition Using Local Binary Patterns. Global Journal of Computer Science and Technology Graphics & Vision V13 (4) Version 1.0. 12) Hamdi Dibeklio˘glu, Member, IEEE, Fares Alnajar, Student Member, IEEE, Albert Ali Salah, Member, IEEE, and Theo Gevers, Member, IEEE, "Combining Facial Dynamics With Appearance for Age Estimation", IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 24, NO. 6, JUNE 2015. 13) Yang Zhong Josephine Sullivan Haibo L KTH Royal Institute of Technology, 100 44 Stockholm, Sweden "Face Attribute Prediction Using Off-the-Shelf CNN Features", IEEE 2016. 14) Max Ehrlich, Timothy J. Shields, Timur Almaev, and Mohamed R. Amer, "Facial Attributes Classification using Multi-Task Representation Learning",2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops. 15) Jianlong Fu1, Heliang Zheng2, TaoMei1"Look Closer to See Better: Recurrent Attention Convolutional Neural Network for Fine-grained Image Recognition", 2017 IEEE Conference on Computer Vision and Pattern Recognition. 16) Yun-Fu Liu, Member, IEEE, Jing-Ming Guo, Senior Member, IEEE, Po-Hsien Liu, Jiann-Der Lee, and Chen-Chieh Yao, "Panoramic Face Recognition", IEEE, Transactions on Circuits and Systems for Video Technology. 17) Xiang-Yu Li Zhen-Xian Lin, "Face Recognition Based on HOG and Fast PCA Algorithm", Springer International Publishing AG 2018 P. Krömer et al. (eds.), Proceedings of the Fourth Euro-China Conference on Intelligent Data Analysis and Applications, Advances in Intelligent Systems and Computing. 18) Mashhood Sajid ; Rubab Hussain ; Muhammad Usman "A conceptual model for automated attendance marking system using facial recognition", Ninth International Conference on Digital Information Management (ICDIM 2014) 19) S Poornima ; N Sripriya ; B Vijayalakshmi ; P Vishnupriya, "Attendance monitoring system using facial recognition with audio output and gender classification", 2017 International Conference on Computer, Communication and Signal Processing (ICCCSP). 20) Hemantkumar Rathod ; Yudhisthir Ware ; Snehal Sane ; Suresh Raulo ; Vishal Pakhare ; Imdad A. R, "Automated attendance system using machine learning approach", 2017 International Conference on Nascent Technologies in Engineering (ICNTE).
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