IJBTT

Hybrid of Viola-Jones and Multi-Block Local Binary Pattern Based on Machine Learning Method for Multiple Angles Rotation Face Detection

Hybrid of Viola-Jones and Multi-Block Local Binary Pattern Based on Machine Learning Method for Multiple Angles Rotation Face Detection
	© 2025 by IJETT Journal
	Volume-73 Issue-6
	Year of Publication : 2025
	Author : Waleed Saeed Mahmoud Mahmoud Ali, Abd Samad Hasan Basari, Wan Zulaikha Wan Yaacob, Mohamed Doheir
	DOI : 10.14445/22315381/IJETT-V73I6P102

How to Cite?
Waleed Saeed Mahmoud Mahmoud Ali, Abd Samad Hasan Basari, Wan Zulaikha Wan Yaacob, Mohamed Doheir, "Hybrid of Viola-Jones and Multi-Block Local Binary Pattern Based on Machine Learning Method for Multiple Angles Rotation Face Detection," International Journal of Engineering Trends and Technology, vol. 73, no. 6, pp.8-16, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I6P102

Abstract
Face detection is considered one of the most vital features in fields such as video surveillance, biometrics, and human-computer interfaces; the drawback is that these tasks require a huge computational and memory application resource. At the simplest level, this technology works by identifying features on a person's face and then using those features in identification from a digital picture of the person. Conversely, head rotation, low-quality pictures, and foreign objects in the detection area still pose big issues. This study examines our existing limitations in most Viola-Jones-based face detection methods, specifically their struggle against problems relating to head position recognition, black faces, and rotated images. The MB-LBP operator encodes image rectangle intensities, and a learning algorithm based on these features improves face detection. Studies indicate that MB-LBP-based classifiers outperform Haar features, making the enhanced Viola-Jones and MB-LBP methods preferred.
The proposed method can detect frontal images rotated to 360 degrees, validated using datasets like Carnegie Mellon University, Labelled Faces in the Wild, and Face Detection Data Set and Benchmark. The results demonstrate a high detection accuracy of 100% in Detection Rate (DR), True Positive Rate (TPR), and False Positive Rate (FPR) for rotated images, highlighting MB-LBP features' superior detail capture and performance.

Keywords
Face detection, Viola-jones algorithm, Multi-block local binary pattern, Machine Learning, Rotated Face Detection, Image Processing.

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