Application of the viola-jones algorithm method to recognize faces of Stmik Tegal students
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Mar 30, 2024
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This study examines the application of the modified Viola-Jones algorithm for student facial recognition at STMIK YMI Tegal, aiming to improve the efficiency and safety of the student attendance system. By adapting the algorithm to address the challenge of facial recognition accuracy from different angles and lighting conditions, a quasi-experimental quantitative design involved collecting data through photographic sessions with student subjects, followed by preprocessing to improve the quality of the analysis. The modification was evaluated for its ability to handle variations in facial and lighting conditions, showing significant improvements with 60% accuracy and precision, recall, and an F1-score of 71.43%. These findings demonstrate the effectiveness of the modification in improving facial recognition, potentially contributing significantly to attendance management and safety practices in educational settings. This research not only strengthens the existing literature.
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Azmi, M. N., Nugroho, B. I. ., Septiana, P. ., & Gunawan, G. (2024). Application of the viola-jones algorithm method to recognize faces of Stmik Tegal students. Journal of Intelligent Decision Support System (IDSS), 7(1), 42-48. https://doi.org/10.35335/idss.v7i1.214
Referensi
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Yadav, K. S., & Singha, J. (2020). Facial expression recognition using modified Viola-John's algorithm and KNN classifier. Multimedia Tools and Applications, 79(19), 13089–13107. https://doi.org/10.1007/s11042-019-08443-x
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Andrejevic, M., & Selwyn, N. (2020). Facial recognition technology in schools: Critical questions and concerns. Learning, Media and Technology, 45(2), 115–128. https://doi.org/10.1080/17439884.2020.1686014
Chen, X., Zou, D., Xie, H., & Wang, F. L. (2021). Past, present, and future of intelligent learning: a topic-based bibliometric analysis. International Journal of Educational Technology in Higher Education, 18(1), 2. https://doi.org/10.1186/s41239-020-00239-6
Goswami, G., Agarwal, A., Ratha, N., Singh, R., & Vatsa, M. (2019). Detecting and mitigating adversarial perturbations for robust face recognition. International Journal of Computer Vision, 127, 719–742. https://doi.org/10.1007/s11263-019-01160-w
Jagadeesh, M., & Baranidharan, B. (2022). Facial expression recognition of online learners from real-time videos using a novel deep learning model. Multimedia Systems, 28(6), 2285–2305. https://doi.org/10.1007/s00530-022-00957-z
Kazansky, B., & Milan, S. (2021). "Bodies not templates": Contesting dominant algorithmic imaginaries. New Media & Society, 23(2), 363–381. https://doi.org/10.1177/1461444820929316
Khmag, A. (2023). Additive Gaussian noise removal based on generative adversarial network model and semi-soft thresholding approach. Multimedia Tools and Applications, 82(5), 7757–7777. https://doi.org/10.1007/s11042-022-13569-6
Kortli, Y., Jridi, M., Al Falou, A., & Atri, M. (2020). Face recognition systems: A survey. Sensors, 20(2), 342. https://doi.org/10.3390/s20020342
Kosinski, M. (2021). Facial recognition technology can expose political orientation from naturalistic facial images. Scientific Reports, 11(1), 100. https://doi.org/10.1038/s41598-021-02785-z
Li, C., Guo, C., & Loy, C. C. (2021). Learning to enhance low-light image via zero-reference deep curve estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(8), 4225–4238. https://doi.org/10.1109/TPAMI.2021.3063604
Lou, G., & Shi, H. (2020). Face image recognition based on convolutional neural network. China Communications, 17(2), 117–124. https://doi.org/10.23919/JCC.2020.02.010
Mamieva, D., Abdusalomov, A. B., Mukhiddinov, M., & Whangbo, T. K. (2023). Improved face detection method via learning small faces on hard images based on a deep learning approach. Sensors, 23(1), 502. https://doi.org/10.3390/s23010502
Minaee, S., Abdolrashidi, A., Su, H., Bennamoun, M., & Zhang, D. (2023). Biometrics recognition using deep learning: A survey. Artificial Intelligence Review, 56(8), 8647–8695. https://doi.org/10.1007/s10462-022-10237-x
Oh Kruzic, C., Kruzic, D., Herrera, F., & Bailenson, J. (2020). Facial expressions contribute more than body movements to conversational outcomes in avatar-mediated virtual environments. Scientific Reports, 10(1), 20626. https://doi.org/10.1038/s41598-020-76672-4
Osaba, E., Villar-Rodriguez, E., Del Ser, J., Nebro, A. J., Molina, D., LaTorre, A., Suganthan, P. N., Coello, C. A. C., & Herrera, F. (2021). A tutorial on the design, experimentation and application of metaheuristic algorithms to real-world optimization problems. Swarm and Evolutionary Computation, 64, 100888. https://doi.org/10.1016/j.swevo.2021.100888
Samadiani, N., Huang, G., Cai, B., Luo, W., Chi, C.-H., Xiang, Y., & He, J. (2019). A review on automatic facial expression recognition systems assisted by multimodal sensor data. Sensors, 19(8), 1863. https://doi.org/10.3390/s19081863
Sampath, V., Maurtua, I., Aguilar Martin, J. J., & Gutierrez, A. (2021). A survey on generative adversarial networks for imbalance problems in computer vision tasks. Journal of Big Data, 8, 1–59. https://doi.org/10.1186/s40537-021-00414-0
Sengupta, J., Ruj, S., & Bit, S. Das. (2020). A comprehensive survey on attacks, security issues and blockchain solutions for IoT and IIoT. Journal of Network and Computer Applications, 149, 102481. https://doi.org/10.1016/j.jnca.2019.102481
Tavallali, P., Yazdi, M., & Khosravi, M. R. (2020). A systematic training procedure for viola-jones face detector in heterogeneous computing architecture. Journal of Grid Computing, 18, 847–862. https://doi.org/10.1007/s10723-020-09517-z
Wan, S., Xia, Y., Qi, L., Yang, Y.-H., & Atiquzzaman, M. (2020). Automated colorization of a grayscale image with seed points propagation. IEEE Transactions on Multimedia, 22(7), 1756–1768. https://doi.org/10.1109/TMM.2020.2976573
Yadav, K. S., & Singha, J. (2020). Facial expression recognition using modified Viola-John's algorithm and KNN classifier. Multimedia Tools and Applications, 79(19), 13089–13107. https://doi.org/10.1007/s11042-019-08443-x
Zhong, K., Wang, Y., Pei, J., Tang, S., & Han, Z. (2021). Super efficiency SBM-DEA and neural network for performance evaluation. Information Processing & Management, 58(6), 102728. https://doi.org/10.1016/j.ipm.2021.102728
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