人脸检测使用增强和直方图归一化

Yassmeen Derhalli, Mariam Nufal, Taher AlSharabati
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引用次数: 3

摘要

本文的目的是应用Viola和Jones人脸检测算法,调整后使用直方图归一化代替方差归一化。使用不同的图像集来训练和测试当前的方法。这些集合取自麻省理工学院生物与计算学习中心(cbcl)的人脸数据库,以及来自互联网的自生成样本。该算法用于检测灰度图像中的人脸,而不是其他算法中使用的彩色图像。并将现有算法与原算法进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Face detection using boosting and histogram normalization
The aim of this paper is to apply the Viola and Jones face detection algorithm with the adjustment of using histogram normalization instead of variance normalization. Various sets of images were used to train and test the current approach. These sets were taken from the MIT Center for Biological and Computation Learning -CBCL- face database in addition to self-generated samples from the internet. The algorithm is applied to detect faces in gray scale images as opposed to colored images used in other algorithms. A comparison between the current approach and the original algorithm is presented.
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