基于均匀局部梯度模式和AdaBoost算法的鲁棒人脸检测

Jun-Gyu Park, D. Kang
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引用次数: 1

摘要

面部特征检测已经应用于许多设备,如相机、智能手机和闭路电视。最重要的是,人脸检测应该在不受外界因素影响的情况下检测人脸,比如光照、背景等的变化。在本研究中,为了对外部因素的鲁棒性和模式的选择,我们提出了一个指定的精度,这是一个更好的算法。该算法用于在检测人脸特征时,从对外界因素具有鲁棒性的LGP算法中提取人脸特征。面部特征是眼睛、鼻子和嘴巴,因此通过指定一种模式来学习。使用训练集检测人脸。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Robust Face Detection Using Uniform Local Gradient Pattern (ULGP) and AdaBoost Algorithm
Facial feature detection has been applied in many devices, such as cameras, smartphones, and CCTV. Most importantly, face detection should detect a face without a significant effect from external factors, such as changes in lighting, background, and so on. In this study, to be robust against external factors and to select the pattern, we propose a specified accuracy that is a better algorithm. The proposed algorithm is used to extract facial features from the LGP algorithm robust to external factors when detecting facial features. The facial features are the eyes, nose, and mouth, thereby learning by specifying a pattern. Using the training set a face is detected.
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