Real-time Triplet Loss Embedding Face Recognition for Authentication Student Attendance Records System Framework

Hady Pranoto - Computer Science Department, School of Computer Science, Bina Nusantara University, Jakarta, 11480, Indonesia
Oktaria Kusumawardani - Computer Science Department, School of Computer Science, Bina Nusantara University, Jakarta, 11480, Indonesia


Citation Format:



DOI: http://dx.doi.org/10.30630/joiv.5.2.480

Abstract


The number of times students attend lectures has been identified as one of many success factors in the learning process in many studies. We proposed a framework of the student attendance system by using face recognition as authentication. Triplet loss embedding in FaceNet is suitable for face recognition systems because the architecture has high accuracy, quite lightweight, and easy to implement in the real-time face recognition system. In our research, triplet loss embedding shows good performance in terms of the ability to recognize faces. It can also be used for real-time face recognition for the authentication process in the attendance recording system that uses RFID. In our study, the performance for face recognition using k-NN and SVM classification methods achieved results of 96.2 +/- 0.1% and 95.2 +/- 0.1% accordingly. Attendance recording systems using face recognition as an authentication process will increase student attendance in lectures. The system should be difficult to be faked; the system will validate the user or student using RFID cards using facial biometric marks. Finally, students will always be present in lectures, which in turn will improve the quality of the existing education process. The outcome can be changed in the future by using a high-resolution camera. A face recognition system with facial expression recognition can be added to improve the authentication process. For better results, users are required to perform an expression instructed by face recognition using a database and the YOLO process.

Keywords


Computer vision; face recognition; recording attendance; Framework.

Full Text:

PDF

References


T. Fadelelmoula, “The impact of class attendance on student performance,” Int. Res. J. Med. Med. Sci., vol. 6, no. May, pp. 47–49, 2018.

G. A. Tetteh, “Effects of Classroom Attendance and Learning Strategies on the Learning Outcome,” J. Int. Educ. Bus., vol. 11, no. 2, pp. 195–219, 2018.

B. Bartanen, “Principal Quality and Student Attendance,” Educ. Res., vol. 49, no. 2, pp. 101–113, 2020.

Y. Zhang et al., “A Teaching Evaluation System Based on Visual Recognition Technology,” IOP Conf. Ser. Mater. Sci. Eng., vol. 782, no. 3, 2020.

K. Lee Lerner and Brenda Wilmoth Lerner, World of forensic science. 2005.

S. Z. Li and A. K. Jain, Introduction Face Recognition. 2011.

K. Ye and F. Hu, “Research on Cross-Age Face Verification Based on Artificial Neural Network Under Examination Environment,” 2017 10th Int. Symp. Comput. Intell. Des., pp. 430–433, 2017.

N. Bakshi and V. Prabhu, “Face recognition system for access control using principal component analysis,” ICCT 2017 - Int. Conf. Intell. Commun. Comput. Tech., vol. 2018-Janua, pp. 145–150, 2018.

N. A. Abdullah, M. J. Saidi, N. H. A. Rahman, C. C. Wen, and I. R. A. Hamid, “Face recognition for criminal identification: An implementation of principal component analysis for face recognition,” AIP Conf. Proc., vol. 1891, 2017.

L. Wang and A. A. Siddique, “Facial recognition system using LBPH face recognizer for anti-theft and surveillance application based on drone technology,” Meas. Control (United Kingdom), vol. 53, no. 7–8, pp. 1070–1077, 2020.

M. Turk and A. Pentland, “Eigenfaces for recognition,” J. Cogn. Neurosci., vol. 3, no. 1, pp. 71–86, 1991.

M. S. Bartlett, J. R. Movellan, and T. J. Sejnowski, “Face recognition by independent component analysis,” IEEE Trans. Neural Networks, vol. 13, no. 6, pp. 1450–1464, Nov. 2002.

P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman, “Eigenfaces vs. fisherfaces: Recognition using class specific linear projection,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 19, no. 7, pp. 711–720, 1997.

H. Hu, P. Zhang, and F. De la Torre, “Face recognition using enhanced linear discriminant analysis,” IET Comput. Vis., vol. 4, no. 3, p. 195, 2010.

Jian Yang, D. Zhang, A. F. Frangi, and Jing-yu Yang, “Two-dimensional pca: a new approach to appearance-based face representation and recognition,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 26, no. 1, pp. 131–137, Jan. 2004.

I. Naseem, R. Togneri, and M. Bennamoun, “Linear regression for face recognition,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 32, no. 11, pp. 2106–2112, 2010.

R. Rahim, T. Afriliansyah, H. Winata, D. Nofriansyah, Ratnadewi, and S. Aryza, “Research of Face Recognition with Fisher Linear Discriminant,” IOP Conf. Ser. Mater. Sci. Eng., vol. 300, no. 1, 2018.

X. He, S. Yan, Y. Hu, P. Niyogi, and H. J. Zhang, “Face recognition using Laplacianfaces,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 3, pp. 328–340, 2005.

T. Ahonen, A. Hadid, and M. Pietikäinen, “Face description with local binary patterns: Application to face recognition,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 28, no. 12, pp. 2037–2041, 2006.

A. Bolotnikova, H. Demirel, and G. Anbarjafari, “Real-time ensemble based face recognition system for NAO humanoids using local binary pattern,” Analog Integr. Circuits Signal Process., vol. 92, no. 3, pp. 467–475, 2017.

B. Zhang, S. Shan, X. Chen, and W. Gao, “Histogram of Gabor phase patterns (HGPP): A novel object representation approach for face recognition,” IEEE Trans. Image Process., vol. 16, no. 1, pp. 57–68, 2007.

B. Yang and S. Chen, “A comparative study on local binary pattern (LBP) based face recognition: LBP histogram versus LBP image,” Neurocomputing, vol. 120, pp. 365–379, 2013.

H. Cevikalp, M. Neamtu, M. Wilkes, and A. Barkana, “Discriminative common vectors for face recognition,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 1, pp. 4–13, 2005.

Y. Wen, “An improved discriminative common vectors and support vector machine based face recognition approach,” Expert Syst. Appl., vol. 39, no. 4, pp. 4628–4632, 2012.

E. Kokiopoulou and Y. Saad, “Orthogonal neighborhood preserving projections: A projection-based dimensionality reduction technique,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 29, no. 12, pp. 2143–2156, 2007.

M. Z. Al-Dabagh, M. H. Mohammed Alhabib, and F. H. AL-Mukhtar, “Face Recognition System Based on Kernel Discriminant Analysis, K-Nearest Neighbor and Support Vector Machine,” Int. J. Res. Eng., vol. 5, no. 2, pp. 335–338, Mar. 2018.

X. M. Zhao and C. B. Wei, “A real-time face recognition system based on the improved LBPH algorithm,” 2017 IEEE 2nd Int. Conf. Signal Image Process. ICSIP 2017, vol. 2017-Janua, pp. 72–76, 2017.

R. Singh, M. Vatsa, and A. Noore, “Face recognition with disguise and single gallery images,” Image Vis. Comput., vol. 27, no. 3, pp. 245–257, 2009.

E. L.-M. Huang, Gary B., Honglak Lee, “Learning Hierarchical Representations for Face Verification.pdf,” pp. 978-1-4673-1228–8/12, 2012.

Y. Taigman, M. Yang, M. Ranzato, and L. Wolf, “DeepFace: Closing the Gap to Human-Level Performance in Face Verification,” in 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014, vol. 73, no. 6, pp. 1701–1708.

Y. Sun, X. Wang, and X. Tang, “Deeply learned face representations are sparse, selective, and robust,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 07-12-June, pp. 2892–2900, 2015.

X. Xu, H. A. Le, P. Dou, Y. Wu, and I. A. Kakadiaris, “Evaluation of a 3D-aided pose invariant 2D face recognition system,” IEEE Int. Jt. Conf. Biometrics, IJCB 2017, vol. 2018-Janua, pp. 446–455, 2018.

S. Qiao and J. Ma, “A Face Recognition System Based on Convolution Neural Network,” Proc. 2018 Chinese Autom. Congr. CAC 2018, pp. 1923–1927, 2019.

M. Nakada, H. Wang, and D. Terzopoulos, “AcFR: Active Face Recognition Using Convolutional Neural Networks,” IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. Work., vol. 2017-July, pp. 35–40, 2017.

H. M. Moon, C. H. Seo, and S. B. Pan, “A face recognition system based on convolution neural network using multiple distance face,” Soft Comput., vol. 21, no. 17, pp. 4995–5002, 2017.

F. Schroff, D. Kalenichenko, and J. Philbin, “FaceNet: A unified embedding for face recognition and clustering,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 07-12-June, pp. 815–823, 2015.

Sharanya T, “Online Attendance using Facial Recognition,” Int. J. Eng. Res., vol. V9, no. 06, pp. 202–207, 2020.

N. T. Son et al., “Implementing CCTV-based attendance taking support system using deep face recognition: A case study at FPT polytechnic college,” Symmetry (Basel)., vol. 12, no. 2, 2020.

L. Zhi-heng and L. Yong-zhen, “Design and Implementation of Classroom Attendance System Based on Video Face Recognition,” 2019 Int. Conf. Intell. Transp. Big Data Smart City, pp. 385–388, 2019.

E. Rekha and P. Ramaprasad, “An efficient automated attendance management system based on Eigen Face recognition,” in 2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence, 2017, vol. 5, pp. 605–608.

N. K. Ayu Wirdiani, T. Lattifia, I. K. Supadma, B. J. Kemanang Mahar, D. A. Nadia Taradhita, and A. Fahmi, “Real-Time Face Recognition with Eigenface Method,” Int. J. Image, Graph. Signal Process., vol. 11, no. 11, pp. 1–9, Nov. 2019.

V. Kazemi and J. Sullivan, “One millisecond face alignment with an ensemble of regression trees,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., pp. 1867–1874, 2014.

M. D. Zeiler and R. Fergus, “Visualizing and understanding convolutional networks,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 8689 LNCS, no. PART 1, pp. 818–833, 2014.

C. Szegedy et al., “Going deeper with convolutions,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 07-12-June, pp. 1–9, 2015.

Y. Sun, X. Wang, and X. Tang, “Deep Learning Face Representation by Joint Identification-Verification,” pp. 1–9, 2014.

K. Q. Weinberger and L. K. Saul, “Distance Metric Learning for Large Margin Nearest Neighbor Classification,” J. Mach. Learn. Res., vol. 10, pp. 173–179, 2007.




Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

__________________________________________________________________________
JOIV : International Journal on Informatics Visualization
ISSN 2549-9610  (print) | 2549-9904 (online)
Organized by Department of Information Technology - Politeknik Negeri Padang, and Institute of Visual Informatics - UKM and Soft Computing and Data Mining Centre - UTHM
Published by Department of Information Technology - Politeknik Negeri Padang
W : http://joiv.org
E : joiv@pnp.ac.id, hidra@pnp.ac.id, rahmat@pnp.ac.id

View JOIV Stats

Creative Commons License is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.