The Effect of Dataset Count on Facial Recognition Accuracy Using Haar Cascade Classifier
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Abstract
This study investigates the impact of varying facial image dataset sizes on the accuracy of facial recognition using the Haar Cascade Classifier method. The dataset sizes examined were 150, 100, and 50 facial images, all captured under consistent conditions using a Raspberry Pi Camera v1.3. The dataset collection, image training, and facial recognition processes were conducted on a Raspberry Pi 4 Single Board Computer (SBC). The study controlled for lighting conditions to ensure they did not affect the results. The facial images were trained using the Local Binary Pattern Histogram (LBPH) recognizer and the Haar Cascade Classifier detector. Recognition tests were conducted at distances of 25cm, 50cm, 75cm, 100cm, 125cm, and 150cm from the camera. The study found that the highest recognition accuracy of 98% was achieved with a 150-image dataset at a distance of 75cm. In contrast, the accuracy dropped to 0% at 125cm and 150cm distances across all dataset sizes. The findings suggest that increasing the number of facial images in the dataset improves the accuracy of facial recognition. Further research is recommended to explore the effectiveness of the Haar Cascade Classifier method in facial recognition systems.
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This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
Kartika, K., Misriana, M., Jannah, M., & Riyaldi, M. A. (2025). The Effect of Dataset Count on Facial Recognition Accuracy Using Haar Cascade Classifier. JTEIN: Jurnal Teknik Elektro Indonesia, 6(1), 48-59. https://doi.org/10.24036/jtein.v6i1.705
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R. R. Isnanto, A. F. Rochim, D. Eridani, and G. D. Cahyono, “Multi-Object Face Recognition Using Local Binary Pattern Histogram and Haar Cascade Classifier on Low-Resolution Images,” Int. J. Eng. Technol. Innov., vol. 11, no. 1, pp. 45–58, 2021, doi: 10.46604/IJETI.2021.6174.
M. F. Hirzi, S. Efendi, and R. W. Sembiring, “Literature Study of Face Recognition using the Viola-Jones Algorithm,” in AIMS 2021 - International Conference on Artificial Intelligence and Mechatronics Systems, IEEE, 2021, pp. 1–6. doi: 10.1109/AIMS52415.2021.9466010.
V. Mutneja, S. Singh, and S. Singh, “Training Analysis of Haar-Classifiers For Face Detection,” Int. J. Electr. Eng. Technol., vol. 12, no. 6, pp. 320–327, 2021.
Y. N. Lin, T. Y. Hsieh, J. J. Huang, C. Y. Yang, V. R. L. Shen, and H. H. Bui, “Fast Iris localization using Haar-like features and AdaBoost algorithm,” Multimed. Tools Appl., vol. 79, no. 45–46, pp. 34339–34362, 2020, doi: 10.1007/s11042-020-08907-5.
N. Chhadikar, P. Bhamare, K. Patil, and S. Kumari, “Image processing based Tracking and Counting Vehicles,” in Proceedings of the 3rd International Conference on Electronics and Communication and Aerospace Technology, ICECA 2019, IEEE, 2019, pp. 335–339. doi: 10.1109/ICECA.2019.8822070.
W. Huan et al., “Haar Wavelet-Based Classification Method for Visual Information Processing Systems,” Appl. Sci., vol. 13, no. 9, p. 5515, 2023, doi: 10.3390/app13095515.
M. Ghosh, T. Sarkar, D. Chokhani, and A. Dey, “Face Detection and Extraction Using Viola–Jones Algorithm,” in Lecture Notes in Electrical Engineering, Springer, 2022, pp. 93–107. doi: 10.1007/978-981-16-4035-3_9.
J. Fan, Z. Zhang, C. Song, and T. Tan, “Learning integral objects with intra-class discriminator for weakly-supervised semantic segmentation,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2020, pp. 4282–4291. doi: 10.1109/CVPR42600.2020.00434.
J. Ciberlin, R. Grbić, N. Teslić, and M. Pilipović, “Object detection and object tracking in front of the vehicle using front view camera,” in 2019 Zooming Innovation in Consumer Technologies Conference, ZINC 2019, IEEE, 2019, pp. 27–32. doi: 10.1109/ZINC.2019.8769367.
S. O. Adeshina, H. Ibrahim, S. S. Teoh, and S. C. Hoo, “Custom face classification model for the classroom using haar-like and LBP features with their performance comparisons,” Electron., vol. 10, no. 2, pp. 1–15, 2021, doi: 10.3390/electronics10020102.
Q. Zhao, “Research on the application of local binary patterns based on color distance in image classification,” Multimed. Tools Appl., vol. 80, no. 18, pp. 27279–27298, 2021, doi: 10.1007/s11042-021-10996-9.
F. Deeba, H. Memon, F. A. Dharejo, A. Ahmed, and A. Ghaffar, “LBPH-based enhanced real-time face recognition,” Int. J. Adv. Comput. Sci. Appl., vol. 10, no. 5, 2019.
K. C. Paul and S. Aslan, “An Improved Real-Time Face Recognition System at Low Resolution Based on Local Binary Pattern Histogram Algorithm and CLAHE,” Opt. Photonics J., vol. 11, no. 04, pp. 63–78, 2021, doi: 10.4236/opj.2021.114005.