Optimization of Real-Time Object Detection in Viola-Jones  Method with Enhanced AdaBoost

Sucitra Sahara; Rizqi Agung Permana; Mely Mailasari

doi:10.30829/zero.v10i1.27876

Optimization of Real-Time Object Detection in Viola-Jones Method with Enhanced AdaBoost

Sucitra Sahara, Rizqi Agung Permana, Mely Mailasari

Abstract

Face recognition is a widely used biometric technology in security systems, automated attendance, and surveillance applications. This study proposes an enhanced real-time face detection method by integrating a modified AdaBoost-based feature selection strategy into the Viola–Jones framework. The applied mathematical contribution of this study lies in formulating the optimization process as an empirical risk minimization model with adaptive boosting weight updates to reduce face recognition error. The proposed approach optimizes the weighting of weak classifiers by prioritizing Haar-like features with minimal weighted classification error at each boosting iteration, thereby improving discriminative capability. Experiments were conducted on a camera-based dataset consisting of face and non-face samples under varying illumination and pose conditions. Prior to optimization, the system achieved a precision of 70.04% and a recall of 70.05%. After applying the proposed enhancement, precision increased to 81.04% and recall to 90.02%. These results demonstrate that the modified AdaBoost integration significantly improves detection accuracy while remaining suitable for real-time face detection applications.

Keywords

Optimization Object Detection; Real-Time; The Viola-Jones Method.

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References

Niyomugabo and T. Kim, “A probabilistic approach to adjust AdaBoost weights,” in Proceedings of the SAI Computing Conference (SAI ’16), pp. 1357–1360, London, UK, July 2016. https://saiconference.com/Conferences

R. Rai, D. Katol, and N. Rai, “Survey paper on vehicle theft detection through face recognition system,” International Journal of Emerging Trends & Technology in Computer Science, vol. 3, no. 1, pp. 256–258, 2014. https://www.internationaljournalssrg.org/IJECE/2022/

Anap Sachin Dattatray et al., "Raspberry Pi-Based Vehicle Starter on Face Detection," Journal of Engineering Science, vol. 12, no. 6, pp. 560-564, 2021. https://jespublication.com/upload/2021-V12I6068.pdf

Ketan J. Bhojane ,s.s.Thorat ; “Face Recognition Based Car Ignition and Security System” ;International Research Journal of Engineering and Technology (IRJET) ,Vol 05, may 2018, pp. 2395-0072. https://www.irjet.net/archives/V5/i5/IRJET-V5I5764.pdf

Wang, Yi-Qing; An Analysis of the Viola-Jones Face Detection Algorithm, Image Processing On Line, 4 (2014), pp. 128–148. https://doi.org/10.5201/ipol.2014.104

Sipahutar, Meri Nova; Linhar, Ade. 2023; “Penerapan AdaBoost Pada Algoritma Viola-Jones Untuk Deteksi Wajah,” Vol.4, No.2, e-ISSN:2721-9380. https://jurnal.uniki.ac.id/index.php/jet.

J. Kaur, A. Sharma, and A. Cse, “Performance Analysis of Face Detection by using Viola-Jones algorithm,” Int. J. Comput. Intell. Res., vol. 13, no. 5, pp. 707–717, 2017, [Online]. Available: http://www.ripublication.com.

Niyomugabo, C., Choi, H. R. & Kim, T. Y. 2016. A Modified AdaBoost Algorithm to Reduce False Positives in Face Detection. Journal International Hindawi Corporation Mathematical Problems in Engineering Vol 25 2016. https://onlinelibrary.wiley.com/doi/epdf/10.1155/2016/5289413

Jalled, F. 2017. Face Recognition Machine Vision System Using Eigenfaces. https://arxiv.org/abs/1705.02782

Dinata, R. K., Safwandi, S., Hasdyna, N., & Mahendra, R. (2020). Kombinasi Algoritma Brute Force dan Stemming pada Sistem Pencarian Mashdar. CESS (Journal of Computer Engineering, System and Science), 5(2), 273-278.

Yashunin, Dmitry; Baydasov, Tamir. MaskFace: multi-task face and landmark detector. 2020. Pp.1-11. Available https://arxiv.org/pdf/2005.09412

Viola, P., & Jones, M. (2004). Robust real-time face detection. International Journal of Computer Vision, 57(2), 137–154

Tolba, M. F., & Moustafa, M. (2016). GAdaBoost: Accelerating AdaBoost feature selection with genetic algorithms.

Siddiqui, A. W., Alam, M. A., Kaur, H., & Ahmed, J. (2021). Implementation of Viola–Jones for detection of facial factors of human for prospect of image recognition

Sipahutar, M. N. M., Linhar, A., & Sipayung, S. P. (2022). Penerapan AdaBoost pada algoritma Viola Jones untuk deteksi wajah

M. H. Yang, D. Kriegman, and N. Ahuja, “Detecting faces in images: A survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence. 2002, vol. 24, no. 1, pp. 34–58.

P. Viola and M. Jones, “Rapid object detection using a boosted cascade of simple features,” in Proc. IEEE Computer Society Conf. Computer Vision and Pattern Recognition (CVPR), Kauai, HI, USA, 2001, pp. 511–518.

S. Z. Li and A. K. Jain, “Handbook of face recognition,” London, U.K.: Springer, 2011.

R. Lienhart and J. Maydt, “An extended set of Haar-like features for rapid object detection,” in Proc. IEEE Int. Conf. Image Processing (ICIP), 2002, pp. 900–903.

Y. Freund and R. E. Schapire, “A decision-theoretic generalization of on-line learning and an application to boosting,” Journal of Computer and System Sciences, 1997. vol. 55, no. 1, pp. 119–139.

P. Dollár, R. Appel, S. Belongie, and P. Perona, “Fast feature pyramids for object detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014. vol. 36, no. 8, pp. 1532–1545.

C. Zhang and Z. Zhang, “A survey of recent advances in face detection,” Microsoft Research Technical Report, 2010.

V. Jain and E. Learned-Miller, “FDDB: A benchmark for face detection in unconstrained settings,” University of Massachusetts Amherst Technical Report, 2010.

H. Luo, Y. Yang, and B. Tong, “Efficient deep learning-based face detection for real-time applications,” IEEE Access, 2021. vol. 9, pp. 108456–108467.

A. Sharifara, M. S. M. Rahim, and T. S. Tan, “A review on face recognition methods,” Journal of Information Security, 2014. vol. 5, no. 4, pp. 219–230.

P. Wang, C. Shen, and N. Barnes, “Fast and robust object detection using asymmetric AdaBoost and a cascade of classifiers,” Pattern Recognition Letters, 2013. vol. 34, no. 6, pp. 669–675.

A. Ranftl, M. Kolossa, and T. Pock, “Real-time AdaBoost cascade face tracker based on likelihood map and optical flow,” 2022. arXiv preprint arXiv:2210.13885.

J. Li, Y. Zhang, and Y. Zhang, “Real-time face detection based on improved Viola–Jones algorithm,” Multimedia Tools and Applications, 2017. vol. 76, no. 21, pp. 22189–22204.

S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards real-time object detection with region proposal networks,” IEEE Trans. Pattern Anal. Mach. Intell., 2020. vol. 39, no. 6, pp. 1137–1149.

T. V. Sandiva, L. Yemi, and A. Ramadhanu, “Face detection using median filtering and Viola–Jones method,” Indonesian J. Comput. Sci., 2024. vol. 13, no. 2, pp. 202–210.

S. Sharma and A. Gupta, “Real-time surveillance system using face detection and recognition,” IEEE Access, 2023. vol. 11, pp. 75422–75435.

DOI: http://dx.doi.org/10.30829/zero.v10i1.27876