Image processing for student emotion monitoring based on fisherface method
(1) * Awang Hendrianto Pratomo Mail (Universitas Pembangunan Nasional Veteran Yogyakarta, Indonesia)
(2) Mangaras Yanu Florestyanto Mail (Universitas Pembangunan Nasional Veteran Yogyakarta, Indonesia)
(3) Y I Sania Mail (Universitas Pembangunan Nasional Veteran Yogyakarta, Indonesia)
(4) B Ihsan Mail (Department of Electrical Engineering, Indonesian Air Force Academy, Indonesia)
(5) H H Triharminto Mail (Department of Electrical Engineering, Indonesian Air Force Academy, Indonesia)
(6) Leonel Hernandez Mail (ITSA University, Colombia)
*corresponding author

Abstract


Monitoring academic emotion is an activity to provide information from students' academic emotions in the class continuously. Some research in the image processing field had done for face recognition but had not been many studies on image processing to detect student emotions. This paper aims to determine the percentage of facial recognition with fisherface and academic emotional recognition by monitoring changes in students' facial expressions using facial landmarks in various distances, camera angles, light, and attributes used on objects. The proposed method uses facial image extraction based on fisherface method for presence. Furthermore, face identification will be made with Euclidean distance by finding the smallest length of training data with test data. Emotion detection is done by facial landmarks and mathematical calculations to detect drowsiness, focus, and not focus on the face. Restful web service is used as a communication architecture to integrate data. The success rate of applications with the fisherface method obtains 96% percent accuracy of face recognition. Meanwhile, facial landmarks and mathematical calculations are used to detect emotions, with 84 %.

Keywords


image processing; emotion recognition; fisherface; facial recognition

   

DOI

https://doi.org/10.31763/sitech.v2i1.690 		      

Article metrics

10.31763/sitech.v2i1.690 Abstract views : 1422 | PDF views : 292

   

Cite

How to cite item
   

Full Text

Download

References


[1] A. R. Dores, F. Barbosa, C. Queirós, I. P. Carvalho, and M. D. Griffiths, “Recognizing Emotions through Facial Expressions: A Largescale Experimental Study,” Int. J. Environ. Res. Public Heal. 2020, Vol. 17, Page 7420, vol. 17, no. 20, p. 7420, Oct. 2020, doi: 10.3390/IJERPH17207420.

[2] H. Avetisyan, O. Bruna, and J. Holub, “Overview of existing algorithms for emotion classification. Uncertainties in evaluations of accuracies.,” J. Phys. Conf. Ser., vol. 772, no. 1, p. 012039, Nov. 2019, doi: 10.1088/1742-6596/772/1/012039.

[3] D. Lu and L. Yan, “Face Detection and Recognition Algorithm in Digital Image Based on Computer Vision Sensor,” J. Sensors, vol. 2021, 2021, doi: 10.1155/2021/4796768.

[4] S. Kolkur, D. Kalbande, P. Shimpi, C. Bapat, and J. Jatakia, “Human Skin Detection Using RGB, HSV and YCbCr Color Models,” vol. 137, pp. 324–332, Dec. 2020, doi: 10.2991/ICCASP-16.2017.51.

[5] B. N. Manu, “Facial features monitoring for real time drowsiness detection,” Proc. 2016 12th Int. Conf. Innov. Inf. Technol. IIT 2022, Mar. 2021, doi: 10.1109/INNOVATIONS.2016.7880030.

[6] P. Viola and M. Jones, “Rapid object detection using a boosted cascade of simple features,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 1, 2019, doi: 10.1109/CVPR.2001.990517.

[7] 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, Mar. 2019, doi: 10.1109/TPAMI.2005.55.

[8] S. C. Ng, “Principal component analysis to reduce dimension on digital image,” Procedia Comput. Sci., vol. 111, pp. 113–119, Jan. 2020, doi: 10.1016/J.PROCS.2017.06.017.

[9] 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, doi: 10.1109/34.598228.

[10] S. Agrawal and P. Khatri, “Facial expression detection techniques: Based on Viola and Jones algorithm and principal component analysis,” Int. Conf. Adv. Comput. Commun. Technol. ACCT, vol. 2015-April, pp. 108–112, Apr. 2022, doi: 10.1109/ACCT.2015.32.

[11] S. X. Tian, S. Lu, and Z. M. Liu, “Levenberg-Marquardt algorithm based nonlinear optimization of camera calibration for relative measurement,” Chinese Control Conf. CCC, vol. 2015-September, pp. 4868–4872, Sep. 2019, doi: 10.1109/CHICC.2015.7260394.

[12] V. T. S. S. IEEE and S. S. IEEE, “Drowsiness detection system and method,” p. 780, Oct. 2019. Available at: patents.google.

[13] W. Deng and R. Wu, “Real-Time Driver-Drowsiness Detection System Using Facial Features,” IEEE Access, vol. 7, pp. 118727–118738, 2019, doi: 10.1109/ACCESS.2019.2936663.

[14] J. D. irawan and E. Adriantantri, “PENDETEKSI MENGANTUK MENGGUNAKAN LIBRARY PYTHON,” Mnemon. J. Tek. Inform., vol. 2, no. 1, pp. 22–27, Jan. 2019, doi: 10.36040/MNEMONIC.V2I1.47.

[15] P. Tsokanaki, D. Moraitou, and G. Papantoniou, “The combined effect of sleep and time of day on emotion decoding from dynamic visual cues in older adults,” Neuropsychiatr. Dis. Treat., vol. 12, p. 2283, Sep. 2022, doi: 10.2147/NDT.S109959.

[16] A. Mohamed, A. Issam, B. Mohamed, and B. Abdellatif, “Real-time Detection of Vehicles Using the Haar-like Features and Artificial Neuron Networks,” Procedia Comput. Sci., vol. 73, pp. 24–31, Jan. 2020, doi: 10.1016/J.PROCS.2015.12.044.

[17] D. Bradley and G. Roth, “Adaptive Thresholding using the Integral Image,”, vol. 12, no. 2, pp. 13–21, Jan. 2021, doi: 10.1080/2151237X.2007.10129236.

[18] H. Yang and X. A. Wang, “Cascade classifier for face detection,” J. Algorithms Comput. Technol., vol. 10, no. 3, pp. 187–197, Sep. 2022, doi: 10.1177/1748301816649073.

[19] E. Annlin, K. James, and S. Annadurai, “An Efficient Implementation of Weighted Fuzzy Fisherface Algorithm for Face Recognition Using Wavelet Transform,” J. Comput. Sci., vol. 8, no. 1, pp. 6–12, Oct. 2021, doi: 10.3844/JCSSP.2012.6.12.


Refbacks

  • There are currently no refbacks.


Copyright (c) 2021 Awang Hendrianto Pratomo

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

___________________________________________________________
Science in Information Technology Letters
ISSN 2722-4139
Published by Association for Scientific Computing Electrical and Engineering (ASCEE)
W : http://pubs2.ascee.org/index.php/sitech
E : sitech@ascee.org, andri@ascee.org, andri.pranolo.id@ieee.org

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

View My Stats