人脸识别分析中的HaarCascade和LBPH算法

M. Gupta, Karan Bisht, Abhay Sharma, Deepak Upadhyay
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引用次数: 0

摘要

不同的人脸检测技术已经使用了很长时间。在此基础上,提出了从数据库中识别人脸或通过视频捕获或使用数据库中的预安装图像训练代码进行人脸识别的方法。人脸检测是现代人工智能系统实现实时人脸识别的必要条件。通过提供与安全相关的威胁,以及通过其他方式,这有助于为识别人员做出信息决策。从图像和实时视频馈送或视频中识别人脸对于机器来说可能是微不足道的任务,并且需要许多图像处理技术来识别特征地标。需要大量的机器学习算法及其在数据集上的测试。本文采用HaarCascade人脸识别和局部二值模式直方图进行特征提取,有助于人脸识别。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
HaarCascade and LBPH Algorithms in Face Recognition Analysis
Different face detection techniques are being used for ages. Taking a step forward, recognizing the human face from the database or getting the new face in the database by capturing in either video or by training the code with the pre-install images in the data base for face recognition has been proposed here. Human face detection is necessity for the modern Artificial Intelligence system that can recognize the face in live feeds. This can be helpful to make informative decisions for the identification of the people, by offering security related threats, and also by other means. Recognizing human faces from the images and live video feeds or videos can be insignificant task for machines and required many image processing techniques for feature landmarks. Numerous Machine Learning Algorithms and their testing on datasets is required. In this paper, HaarCascade face recognition and Local Binary Pattern Histogram are used for feature extraction which will help in identification of human face.
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