Optimizing the viola-jones algorithm for robust face recognition in variable lighting and orientation conditions
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Published:
May 30, 2024
Keywords:
Algorithm Viola-Jones, Face detection, Face recognition, Image processing, Parameter optimization
Section
Computer Science
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Abstract
Facial recognition is a critical technology in digital security, driven by significant advances in computer vision. This research focuses on optimizing the Viola-Jones algorithm to improve the accuracy and speed of face detection by adjusting parameters and integrating more sophisticated image processing techniques. Facing challenges such as suboptimal lighting and variations in face orientation, the study adopted a rigorous experimental design, in-depth quantitative analysis, and robust model validation. Of the ten facial images collected, all were intensively processed using Haar-like features to identify significant patterns and adjust algorithm parameters in Python. This optimization process increased performance from 7 identified faces to 9 post-optimization identified faces and a substantial decrease in detection time from 0.0065 seconds to 0.0017 seconds per image. The comprehensive evaluation showed an increase in accuracy from 70% to 90%, recall from 70.0% to 90.0%, Precision remained constant at 100.0%, and F1-score from 82.35% to 94.74%. These results show that the optimization has increased the algorithm's sensitivity to changes in light intensity and face orientation and improved the effectiveness of facial recognition systems in complex and dynamic security scenarios while providing concrete evidence of the benefits of using Haar-like features in the Viola-Jones algorithm
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How to Cite
Gunawan, G. ., Aisyah, N. . and Santoso, N. A. . (2024) “Optimizing the viola-jones algorithm for robust face recognition in variable lighting and orientation conditions”, Jurnal Mantik, 8(1), pp. 787-797. doi: 10.35335/mantik.v8i1.5220.
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Alakus, T. B., & Turkoglu, I. (2020). Comparison of deep learning approaches to predict COVID-19 infection. Chaos, Solitons & Fractals, 140, 110120. https://doi.org/10.1016/j.chaos.2020.110120
Aouani, H., & Ben Ayed, Y. (2024). Deep facial expression detection using Viola-Jones algorithm, CNN-MLP and CNN-SVM. Social Network Analysis and Mining, 14(1), 1–10. https://doi.org/10.1007/s13278-024-01231-y
Du, H., Shi, H., Zeng, D., Zhang, X.-P., & Mei, T. (2022). The elements of end-to-end deep face recognition: A survey of recent advances. ACM Computing Surveys (CSUR), 54(10s), 1–42. https://doi.org/10.1145/3507902
Ebrahimi-Moghadam, A., Ildarabadi, P., Aliakbari, K., & Fadaee, F. (2020). Sensitivity analysis and multi-objective optimization of energy consumption and thermal comfort by using interior light shelves in residential buildings. Renewable Energy, 159, 736–755. https://doi.org/10.1016/j.renene.2020.05.127
Eyiokur, F. I., Kantarc?, A., Erak?n, M. E., Damer, N., Ofli, F., Imran, M., Križaj, J., Salah, A. A., Waibel, A., & Štruc, V. (2023). A survey on computer vision based human analysis in the COVID-19 era. Image and Vision Computing, 130, 104610. https://doi.org/10.1016/j.imavis.2022.104610
Hasan, M. R., Guest, R., & Deravi, F. (2023). Presentation-level privacy protection techniques for automated face recognition—A survey. ACM Computing Surveys, 55(13s), 1–27. https://doi.org/10.1145/3583135
Isizoh, A. N., Ojo, F., & Okechukwu, O. P. (2023). EFFECTIVE USE OF ARTIFICIAL INTELLIGENCE FOR THE ENHANCEMENT OF FACIAL RECOGNITION SYSTEM. International Journal of Computing, Science and New Technologies (IJCSNT), 1(2), 11–22.
Jayaraman, U., Gupta, P., Gupta, S., Arora, G., & Tiwari, K. (2020). Recent development in face recognition. Neurocomputing, 408, 231–245. https://doi.org/10.1016/j.neucom.2019.08.110
Klaib, A. F., Alsrehin, N. O., Melhem, W. Y., Bashtawi, H. O., & Magableh, A. A. (2021). Eye tracking algorithms, techniques, tools, and applications with an emphasis on machine learning and Internet of Things technologies. Expert Systems with Applications, 166, 114037. https://doi.org/10.1016/j.eswa.2020.114037
Lee, C. T., & Pan, L.-Y. (2023). Resistance of facial recognition payment service: a mixed method approach. Journal of Services Marketing, 37(3), 392–407. https://doi.org/10.1108/JSM-01-2022-0035
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. https://doi.org/10.1007/s11042-020-08907-5
Mahanti, N. K., Pandiselvam, R., Kothakota, A., Chakraborty, S. K., Kumar, M., & Cozzolino, D. (2022). Emerging non-destructive imaging techniques for fruit damage detection: Image processing and analysis. Trends in Food Science & Technology, 120, 418–438. https://doi.org/10.1016/j.tifs.2021.12.021
Marsot, M., Mei, J., Shan, X., Ye, L., Feng, P., Yan, X., Li, C., & Zhao, Y. (2020). An adaptive pig face recognition approach using Convolutional Neural Networks. Computers and Electronics in Agriculture, 173, 105386. https://doi.org/10.1016/j.compag.2020.105386
Oloyede, M. O., Hancke, G. P., & Myburgh, H. C. (2020). A review on face recognition systems: recent approaches and challenges. Multimedia Tools and Applications, 79(37), 27891–27922. https://doi.org/10.1007/s11042-020-09261-2
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
Qaim, M. (2020). Role of new plant breeding technologies for food security and sustainable agricultural development. Applied Economic Perspectives and Policy, 42(2), 129–150. https://doi.org/10.1002/aepp.13044
Raj, R., Rajiv, P., Kumar, P., Khari, M., Verdú, E., Crespo, R. G., & Manogaran, G. (2020). Feature based video stabilization based on boosted HAAR Cascade and representative point matching algorithm. Image and Vision Computing, 101, 103957. https://doi.org/10.1016/j.imavis.2020.103957
Seng, S., Al-Ameen, M. N., & Wright, M. (2021). A first look into users’ perceptions of facial recognition in the physical world. Computers & Security, 105, 102227. https://doi.org/10.1016/j.cose.2021.102227
Singh, P. (2021). Aadhaar and data privacy: biometric identification and anxieties of recognition in India. Information, Communication & Society, 24(7), 978–993. https://doi.org/10.1080/1369118X.2019.1668459
Soleimanipour, A., & Chegini, G. R. (2020). A vision-based hybrid approach for identification of Anthurium flower cultivars. Computers and Electronics in Agriculture, 174, 105460. https://doi.org/10.1016/j.compag.2020.105460
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
Wang, Z., & Sobey, A. (2020). A comparative review between Genetic Algorithm use in composite optimisation and the state-of-the-art in evolutionary computation. Composite Structures, 233, 111739. https://doi.org/10.1016/j.compstruct.2019.111739
Wanyonyi, D., & Celik, T. (2022). Open-source face recognition frameworks: A review of the landscape. IEEE Access, 10, 50601–50623. https://doi.org/10.1109/ACCESS.2022.3170037
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
Yesilevskyi, V., & Kyt, M. (20s24). Changing Trends in Teaching Computer Vision at Ukrainian Universities in the Age of Artificial Intelligence. International Journal of Emerging Technologies in Learning, 19(4). https://doi.org/10.3991/ijet.v19i04.48391
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