![References
[1] Bae, Mi-Young, and Dae-Jea Cho. "Design and implementation of automatic attendance check system using BLE
beacon." International Journal of Multimedia and Ubiquitous Engineering 10, no. 10 (2015): 177-186.
[2] Jogiji, Aditya, and P. Ghate. "WSN Based Automatic Attendance Monitoring System." International Journal of
Computer & Mathematical Sciences (IJCMS) 6, no. 8 (2017).
[3] Ahmedi, Aziza, and Suvarna Nandyal. "An Automatic Attendance System Using Image processing." The International
Journal Of Engineering And Science 4, no. 11 (2015): 1-8.
[4] Krishnan, R. Ramya, R. Renuka, C. Swetha, and R. Ramakrishnan. "Effective Automatic Attendance Marking System
Using Face Recognition with RFID." IJSRST 2920 (2016): 158-162.
[5] Shengli, K., Jun, Z., Guang, S., Chunhong, W., Wenpei, Z. and Tao, L., 2015. The Design and Implementation of the
Attendance Management System based on Radio Frequency Identification Technology. In International Conference on
Electronic Science and Automation Control,(ESAC 2015) Google Scholar.](https://image.slidesharecdn.com/438-190312164500/85/Automatic-Attendance-System-using-Deep-Learning-13-320.jpg)
Automatic Attendance System using Deep Learning
- 1. Automatic Attendance System Using Deep Learning Presented By: Sunil Aryal Paper No: 438
- 2. 1 A brief overview Introduction 2 Reason behind development of this idea. Purpose 3 Technicality how entire project is working Methodology AGENDA 4 Conclusion and Result Conclusion
- 3. Introduction • Automatic attendance system is automated version of physical presence of person without any manual interference. • Real-time face recognition algorithms are used and integrated with existing University management system.
- 4. Introduction • Machine Learning: Use of algorithm in such way that it makes prediction on basis of previous observations. • With advancement in parallel computing, complex learning like facial recognition (Computer Vision) are being used in today’s time with less cost error and high accuracy.
- 5. In huge classes, there is high probability of error in entire process by use of conventional method but our system is highly accurate with accuracy of 98% with no or less biased. Accurate(Less Biased) As system is automated, the exist no biasness in system and system remains transparent. Transparency Traditional system of marking attendance require manual interference but this system do not involve human interaction with system. Remove manual work 20% of total time of lecture gets wasted in taking attendance but with this system we can mark attendance in no time or real time attendance system. Time Efficient Why this project was built? Purpose
- 6. Methodology This system consist of 5 major steps. 2 3 4 1 5 Take picture using high definition camera Detect faces Recognize Faces Database Processing Mark attendance
- 7. Detect Faces • Faces are being detected by using CNN(Convolutional Neural Networks) and are optimized using transfer learning to speed up learning process. • Landmark detection is used to pre-process the detected faces. • To detect multiple faces from single image MTCNN is used.
- 8. Detect Faces
- 9. Face Recognition • Our network architecture for face recognition is based on ResNet-34 from the Deep Residual Learning for Image Recognition paper by He et al., but with fewer layers and the number of filters reduced by half with addition of feedback. • The network itself was trained by Davis King on a dataset of ~3 million images. On the Labelled Faces in the Wild (LFW) dataset the network compares to other state-of-the- art methods, reaching 97% accuracy.
- 10. Face Recognition
- 11. Database Processing and Marking Attendance • After recognition of faces with unique id from image captured are ingested to database with existing university management system.
- 12. Results 97% Deep Metric Learning 23% Eigen Face 13% Fisher Face 20% LBPH
- 13. References [1] Bae, Mi-Young, and Dae-Jea Cho. "Design and implementation of automatic attendance check system using BLE beacon." International Journal of Multimedia and Ubiquitous Engineering 10, no. 10 (2015): 177-186. [2] Jogiji, Aditya, and P. Ghate. "WSN Based Automatic Attendance Monitoring System." International Journal of Computer & Mathematical Sciences (IJCMS) 6, no. 8 (2017). [3] Ahmedi, Aziza, and Suvarna Nandyal. "An Automatic Attendance System Using Image processing." The International Journal Of Engineering And Science 4, no. 11 (2015): 1-8. [4] Krishnan, R. Ramya, R. Renuka, C. Swetha, and R. Ramakrishnan. "Effective Automatic Attendance Marking System Using Face Recognition with RFID." IJSRST 2920 (2016): 158-162. [5] Shengli, K., Jun, Z., Guang, S., Chunhong, W., Wenpei, Z. and Tao, L., 2015. The Design and Implementation of the Attendance Management System based on Radio Frequency Identification Technology. In International Conference on Electronic Science and Automation Control,(ESAC 2015) Google Scholar.
- 14. Questions?