Main Article Content

Fajar Sugeng Riyadi
Gunawan Gunawan
Zaenul Arif

Abstract

This research aims to develop a facial recognition system using computer vision technology by applying the Viola-Jones algorithm method. The main focus of this research is to improve accuracy and efficiency in face identification under various lighting conditions and face orientations. The Viola-Jones algorithm, known for its real-time object detection, was chosen for its efficiency in quickly identifying critical facial features. Through testing of various face datasets, the results showed that the system developed was able to recognize faces with a high level of accuracy, even in conditions of non-optimal lighting and various facial poses. The novelty of this research lies in the optimization of the parameters of the Viola-Jones algorithm to improve facial recognition performance, as well as its application in challenging dynamic environments. These findings make a significant contribution to the field of computer vision and facial recognition, offering more effective and efficient solutions for security and surveillance applications, as well as interactive applications that require fast and accurate facial identification.

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How to Cite
Riyadi, F. S. ., Gunawan, G., & Arif, Z. . (2024). Application of computer vision for face recognition using viola jones algorithm method. Journal of Intelligent Decision Support System (IDSS), 7(1), 27-34. https://doi.org/10.35335/idss.v7i1.204
References
Ameen, N., Tarhini, A., Shah, M. H., & Madichie, N. O. (2020). Employees’ behavioural intention to smartphone security: A gender-based, cross-national study. Computers in Human Behavior, 104, 106184.
Anaya-Isaza, A., & Mera-Jiménez, L. (2022). Data augmentation and transfer learning for brain tumor detection in magnetic resonance imaging. IEEE Access, 10, 23217–23233.
Andrejevic, M., & Selwyn, N. (2020). Facial recognition technology in schools: Critical questions and concerns. Learning, Media and Technology, 45(2), 115–128.
Chaves, D., Fidalgo, E., Alegre, E., Alaiz-Rodríguez, R., Jáñez-Martino, F., & Azzopardi, G. (2020). Assessment and estimation of face detection performance based on deep learning for forensic applications. Sensors, 20(16), 4491.
Grundmann, F., Epstude, K., & Scheibe, S. (2021). Face masks reduce emotion-recognition accuracy and perceived closeness. PloS One, 16(4), e0249792.
Hasani, M., & Jafari, A. (2022). Electromagnetic field’s effect on enhanced oil recovery using magnetic nanoparticles: Microfluidic experimental approach. Fuel, 307, 121718.
Kortli, Y., Jridi, M., Al Falou, A., & Atri, M. (2020). Face recognition systems: A survey. Sensors, 20(2), 342.
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.
Li, S., & Deng, W. (2020). Deep facial expression recognition: A survey. IEEE Transactions on Affective Computing, 13(3), 1195–1215.
Liang, Z., Ding, X., Wang, Y., Yan, X., & Fu, X. (2021). GUDCP: Generalization of underwater dark channel prior for underwater image restoration. IEEE Transactions on Circuits and Systems for Video Technology, 32(7), 4879–4884.
Lin, Y.-N., Hsieh, T.-Y., Huang, J.-J., Yang, C.-Y., Shen, V. R. L., & Bui, H. H. (2020). Fast Iris localization using Haar-like features and AdaBoost algorithm. Multimedia Tools and Applications, 79, 34339–34362.
Liu, Y., Sun, P., Wergeles, N., & Shang, Y. (2021). A survey and performance evaluation of deep learning methods for small object detection. Expert Systems with Applications, 172, 114602.
Masud, U., Saeed, T., Malaikah, H. M., Islam, F. U., & Abbas, G. (2022). Smart assistive system for visually impaired people obstruction avoidance through object detection and classification. IEEE Access, 10, 13428–13441.
Singh, S., Sharma, P. K., Yoon, B., Shojafar, M., Cho, G. H., & Ra, I.-H. (2020). Convergence of blockchain and artificial intelligence in IoT network for the sustainable smart city. Sustainable Cities and Society, 63, 102364.
Tavallali, P., Yazdi, M., & Khosravi, M. R. (2020a). A systematic training procedure for viola-jones face detector in heterogeneous computing architecture. Journal of Grid Computing, 18, 847–862.
Tavallali, P., Yazdi, M., & Khosravi, M. R. (2020b). A systematic training procedure for viola-jones face detector in heterogeneous computing architecture. Journal of Grid Computing, 18, 847–862.
Thabtah, F., Hammoud, S., Kamalov, F., & Gonsalves, A. (2020). Data imbalance in classification: Experimental evaluation. Information Sciences, 513, 429–441.
Wang, M., & Deng, W. (2021). Deep face recognition: A survey. Neurocomputing, 429, 215–244.
Yang, P., Xiong, N., & Ren, J. (2020). Data security and privacy protection for cloud storage: A survey. IEEE Access, 8, 131723–131740.
Zhang, L., Wang, J., & An, Z. (2023). Vehicle recognition algorithm based on Haar-like features and improved Adaboost classifier. Journal of Ambient Intelligence and Humanized Computing, 14(2), 807–815.
Zou, Z., Chen, K., Shi, Z., Guo, Y., & Ye, J. (2023). Object detection in 20 years: A survey. Proceedings of the IEEE.