Face Recognition-Based Room Access Security System Prototype using A Deep Learning Algorithm

  • Immanuel Morries Pohan Universitas Sriwijaya
  • Suci Dwijayanti Universitas Sriwijaya
  • Bhakti Yudho Suprapto Universitas Sriwijaya
  • Hera Hikmarika Universitas Sriwijaya
  • Hermawati Hermawati Universitas Sriwijaya
DOI: https://doi.org/10.29207/resti.v7i6.5376
Keywords: security system, convolutional neural network (CNN), face recognition, biometric, VGG16

Abstract

Writing Mandarin characters is considered the most challenging component for beginners due to the rules and character formations. This paper explores the potential of a machine learning-based digital learning tool to write Mandarin characters. It also conducts a comparative study between MobileNetV2 and MobileNetV3, exploring different configurations. The research follows the Multimedia Development Life Cycle (MDLC) method to create both application and machine learning models. Participants from higher education institutions that offer Mandarin courses in Batam, Indonesia, participated in a User Acceptance Test (UAT). Data were collected through questionnaires and analyzed using the System Usability Scale (SUS) methods. The results show positive user acceptance, with an SUS score of 77.92%, indicating a high level of acceptability. MobileNetV3Small was also preferred for recognizing user handwriting, due to comparable accuracy size, rapid inference time and smallest model size. Although the application was well received, several participants provided constructive feedback, suggesting potential improvements. 

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References

Andreas, C. R. Aldawira, H. W. Putra, N. Hanafiah, S. Surjarwo, and A. Wibisurya, “Door security system for home monitoring based on ESp32,” Procedia Comput. Sci., vol. 157, pp. 673–682, 2019, doi: 10.1016/j.procs.2019.08.218.

S. Dargan and M. Kumar, “A comprehensive survey on the biometric recognition systems based on physiological and behavioral modalities,” Expert Syst. Appl., vol. 143, p. 113114, 2020, doi: 10.1016/j.eswa.2019.113114.

S. A. Abdulrahman and B. Alhayani, “A comprehensive survey on the biometric systems based on physiological and behavioural characteristics,” Mater. Today Proc., vol. 80, no. July, pp. 2642–2646, 2023, doi: 10.1016/j.matpr.2021.07.005.

Z. Rui and Z. Yan, “A Survey on Biometric Authentication: Toward Secure and Privacy-Preserving Identification,” IEEE Access, vol. 7, pp. 5994–6009, 2019, doi: 10.1109/ACCESS.2018.2889996.

V. Seelam, A. K. Penugonda, B. Pavan Kalyan, M. Bindu Priya, and M. Durga Prakash, “Smart attendance using deep learning and computer vision,” Mater. Today Proc., vol. 46, pp. 4091–4094, 2020, doi: 10.1016/j.matpr.2021.02.625.

H. Ku and W. Dong, “Face Recognition Based on MTCNN and Convolutional Neural Network,” Front. Signal Process., vol. 4, no. 1, pp. 37–42, 2020, doi: 10.22606/fsp.2020.41006.

D. A. R. Wati and D. Abadianto, “Design of face detection and recognition system for smart home security application,” Proc. - 2017 2nd Int. Conf. Inf. Technol. Inf. Syst. Electr. Eng. ICITISEE 2017, vol. 2018-Janua, pp. 342–347, 2018, doi: 10.1109/ICITISEE.2017.8285524.

M. Sajjad et al., “Raspberry Pi assisted face recognition framework for enhanced law-enforcement services in smart cities,” Futur. Gener. Comput. Syst., vol. 108, pp. 995–1007, 2020, doi: 10.1016/j.future.2017.11.013.

A. A. Sukmandhani and I. Sutedja, “Face Recognition Method for Online Exams,” Proc. 2019 Int. Conf. Inf. Manag. Technol. ICIMTech 2019, vol. 1, no. August, pp. 175–179, 2019, doi: 10.1109/ICIMTech.2019.8843831.

K. H. Teoh, R. C. Ismail, S. Z. M. Naziri, R. Hussin, M. N. M. Isa, and M. S. S. M. Basir, “Face Recognition and Identification using Deep Learning Approach,” J. Phys. Conf. Ser., vol. 1755, no. 1, 2021, doi: 10.1088/1742-6596/1755/1/012006.

E. Jose, M. Greeshma, T. P. Mithun Haridas, and M. H. Supriya, “Face Recognition based Surveillance System Using FaceNet and MTCNN on Jetson TX2,” 2019 5th Int. Conf. Adv. Comput. Commun. Syst. ICACCS 2019, no. March, pp. 608–613, 2019, doi: 10.1109/ICACCS.2019.8728466.

S. Dwijayanti, R. R. Abdillah, H. Hikmarika, Hermawati, Z. Husin, and B. Y. Suprapto, “Facial Expression Recognition and Face Recognition Using a Convolutional Neural Network,” in 2020 3rd International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2020, Dec. 2020, pp. 621–626. doi: 10.1109/ISRITI51436.2020.9315513.

S. Gupta, K. Thakur, and M. Kumar, “2D-human face recognition using SIFT and SURF descriptors of face’s feature regions,” Vis. Comput., vol. 37, no. 3, pp. 447–456, 2021, doi: 10.1007/s00371-020-01814-8.

H. M. Ahmed and R. T. Rasheed, “A Raspberry PI Real-Time Identification System on Face Recognition,” Proc. 2020 1st Inf. Technol. to Enhanc. E-Learning other Appl. Conf. IT-ELA 2020, pp. 89–93, 2020, doi: 10.1109/IT-ELA50150.2020.9253107.

Z. Yu, F. Liu, R. Liao, Y. Wang, H. Feng, and X. Zhu, “Improvement of Face Recognition Algorithm Based on Neural Network,” Proc. - 10th Int. Conf. Meas. Technol. Mechatronics Autom. ICMTMA 2018, vol. 2018-January, pp. 229–234, 2018, doi: 10.1109/ICMTMA.2018.00062.

H. Zhi and S. Liu, “Face recognition based on genetic algorithm,” J. Vis. Commun. Image Represent., vol. 58, pp. 495–502, 2019, doi: 10.1016/j.jvcir.2018.12.012.

M. H. Wan and Z. H. Lai, “Generalized Discriminant Local Median Preserving Projections (GDLMPP) for Face Recognition,” Neural Process. Lett., vol. 49, no. 3, pp. 951–963, 2019, doi: 10.1007/s11063-018-9840-6.

M. Owais, A. A. Jalal, M. M. Hassan, and A. Shaikh, “Facial Recognition based Attendance System Using CNN and Raspberry Pi,” 4th Int. Symp. Multidiscip. Stud. Innov. Technol. ISMSIT 2020 - Proc., 2020, doi: 10.1109/ISMSIT50672.2020.9254300.

S. A. Dar and S. Palanivel, “Neural Networks (CNNs) and Vgg on Real Time Face Recognition System,” Turkish J. Comput. Math. Educ., vol. 12, no. 9, pp. 1809–1822, 2021.

K. R. Avery et al., “Fatigue Behavior of Stainless Steel Sheet Specimens at Extremely High Temperatures,” SAE Int. J. Mater. Manuf., vol. 7, no. 3, pp. 560–566, 2014, doi: 10.4271/2014-01-0975.

K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” Sep. 2014, [Online]. Available: http://arxiv.org/abs/1409.1556

Published
2023-12-26
How to Cite
Pohan, I. M., Dwijayanti, S., Suprapto, B. Y., Hikmarika, H., & Hermawati, H. (2023). Face Recognition-Based Room Access Security System Prototype using A Deep Learning Algorithm. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 7(6), 1387 - 1393. https://doi.org/10.29207/resti.v7i6.5376
Issue
Vol 7 No 6 (2023): December 2023
Section
Information Systems Engineering Articles

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