面部检测和比较 [英] Face detection and comparison
问题描述
我正在对我的文章进行一项关于面部检测和比较的小型研究。
目前，我正在使用快速面部检测基于haar的功能基于OpenCV级联（我以后实现学习）。下一步是进行面部比较。有没有任何熟知的算法？这将是伟大的，如果有一些C＃代码，解释它们或一些dll的实现它们。
I'm running a small research on face detection and comparison for my article. Currently, I'm using rapid face detection based on haar like features based on OpenCV cascade (I'll implement learning later). The next step is making face comparison. Are there any well know algorithms? It'll be great, if there are some C# codes, explaining them or some dll's that implements them.
推荐答案
为了产生一组特征面，
人类
面部的大量数字化图像集合，在相同照明条件下拍摄，被标准化以将
眼睛和嘴巴排列。然后他们都是
重新采样在同一像素
分辨率。特征面可以通过
从图像数据中提取的
的数学工具称为
主成分分析（PCA）。
To generate a set of eigenfaces, a large set of digitized images of human faces, taken under the same lighting conditions, are normalized to line up the eyes and mouths. They are then all resampled at the same pixel resolution. Eigenfaces can be extracted out of the image data by means of a mathematical tool called principal component analysis (PCA).
特征面现在可以用于
表示新面孔：我们可以在
特征面上投影一个
新（平均减法）图像，从而记录
新面如何不同于平均脸。
与每个
特征面相关联的特征值表示训练集中的
图像与该方向上的平均图像的
变化有多大。 We
通过将
图像投影到特征向量
的子集上来丢失信息，但是我们通过保持
那些具有最大
特征值的特征面来最小化这种损失。 p>
The eigenfaces can now be used to represent new faces: we can project a new (mean-subtracted) image on the eigenfaces and thereby record how that new face differs from the mean face. The eigenvalues associated with each eigenface represent how much the images in the training set vary from the mean image in that direction. We lose information by projecting the image on a subset of the eigenvectors, but we minimise this loss by keeping those eigenfaces with the largest eigenvalues.
如果你的面部不对齐，那么我建议阅读下面的文章：
If your faces aren't aligned, then I'd recommend reading the following paper:
摘要：我们提出一个基于组件的方法
和两个面向
的全局方法他们与
尊重对姿势的稳健性
改变。在组件系统中，我们
首先定位面部组件，
提取它们并将它们组合成
单个特征向量，其是通过支持向量机
（SVM）分类的
）。
Abstract: We present a component-based method and two global methods for face recognition and evaluate them with respect to robustness against pose changes. In the component system we first locate facial components, extract them and combine them into a single feature vector which is classified by a Support Vector Machine (SVM).
两个全局系统通过分类由
整个面部图像的灰度值组成的单个特征向量
来识别面部
。在第一个全局
系统中，我们为
数据库中的每个人训练单个SVM
分类器。第二个系统由观察点特定SVM
分类器的集合
组成，并且在训练期间涉及聚类
。
The two global systems recognize faces by classifying a single feature vector consisting of the gray values of the whole face image. In the first global system we trained a single SVM classifier for each person in the database. The second system consists of sets of viewpoint-specific SVM classifiers and involves clustering during training.
这篇关于面部检测和比较的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
