基于更宽人脸数据集的Viola-Jones人脸检测算法

2022 10th International Conference on Cyber and IT Service Management (CITSM) Pub Date : 2022-09-20 DOI:10.1109/CITSM56380.2022.9935830

Sumanto, B. Wijonarko, Muhammad Qommarudin, Aji Sudibyo, Pudji Widodo, Afit Muhammad Lukman

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引用次数: 1

摘要

多年来，人脸检测一直是计算机视觉领域研究最多的问题之一。使用WIDER FACES数据集，本研究探讨了如何使用Viola-Jones方法识别179张照片中的人脸，以及与其他人脸检测算法相比，该方法的表现如何。在之前的一项使用Viola-jones进行的人脸检测研究中，人脸图像的准确率最高，为90.9%，非人脸图像的准确率最高，为75.5%。在这项研究中，维奥拉-琼斯方法有100%的成功率。这种方法将在MATLAB算法中用于人脸识别，得到比目前更好的结果。使用两个类的实验取得了令人鼓舞的结果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Viola-Jones Algorithm for Face Detection using Wider Face Dataset

Face detection has been one of the most explored problems in computer vision for several years. Using the WIDER FACES data set, this study investigates how the Viola-Jones method can be used to identify faces in 179 photos and how it performs compared to other face detection algorithms. In a previous study for face detection using Viola-jones, the highest accuracy results were obtained at 90.9% for facial images and 75.5% for non-face images. In this study, the Viola-Jones approach had a 100 percent success rate. This approach will be used in the MATLAB algorithm for face identification to get better results than currently available. Experiments using two classes had promising results.

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2022 10th International Conference on Cyber and IT Service Management (CITSM)

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