人脸识别领域的最新技术
I have looked a little bit into face recognition recently. However I am overwelmed by the number of algorithm that there are.
PCA, LDA, IDA, Gabor Wavelets, AAM ...
Say you wanted to make something like this... Which algorithms would you use or which papers would you read?
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我认为您应该做的第一件事是意识到您列出的算法用于人脸识别的不同阶段。
首先，您需要决定表示形式，即要使用的功能。
这些可能是原始像素、Gabor 滤波器、某种形状描述符、可变形模型等。
然后，您通常希望降低特征的维度。这就是 PCA、ICA 或 LDA 等算法的用武之地，它们将数据点投影到较低维度的空间中，试图保留大部分方差 (PCA) 或确保不同类别点的最佳分离 (LDA)。
然后你可能想根据你的特征训练一个分类器来区分不同人的面孔。这里有大量算法可供选择，例如最近邻、支持向量机、隐马尔可夫模型、贝叶斯网络等。
请注意，特定阶段算法的选择可能取决于也可能不取决于以下算法：其他阶段。例如，PCA 可用于降低几乎任何类型特征的维度。另一方面，如何对由可变形网格表示的面使用支持向量机分类器并不是立即显而易见的。
我想你应该尝试做的第一件事就是非常精确地定义你的问题。您是否想区分少数人的面孔，例如识别照片中的家人？您想从庞大的数据库中识别人物吗？每张脸有很多训练图像，还是只有少数？您想处理不同的方向和照明条件吗？
这些问题的答案决定了你的问题的复杂程度，并且肯定会影响你对算法的选择。
编辑：
这是一个尝试解决类似问题的人的论文。这是从 2002 年开始的，但恕我直言，这是一个很好的起点。
I think the first thing you should do is realize that the algorithms you've listed are used at different stages of face recognition.
First, you need to decide on the representation, i. e. the features to use.
These could be raw pixels, Gabor filters, some kind of shape descriptors, deformable models, etc.
Then, you typically want to reduce the dimensionality of your features. This is where algorithms like PCA, ICA, or LDA come in, which project data points into a lower dimensional space trying to preserve most of the variance (PCA) or to ensure the optimal separation of points of different categories (LDA).
Then you probably want to train a classifier on your features to distinguish between the faces of different people. There is a plethora of algorithms to choose from here, such as the Nearest Neighbor, Support Vector Machines, Hidden Markov Models, Bayes Nets, etc.
Note that the choice of an algorithm for a particular stage may or may not depend on the algorithms for other stages. For instances, PCA can be used to reduce dimensionality of almost any type of features. On the other hand, it is not immediately obvious how one can use a support vector machine classifier for faces represented by a deformable mesh.
I guess the first thing you should try to do is to define your problem very precisely. Do you want to distinguish between faces of only a few people, such as recognize your family members in photos? Do you want to recognize people from a huge database? Do you have lots of training images for each face, or only a few? Do you want to handle different orientations and lighting conditions?
The answers to these questions determine how complicated your problem is, and will certainly affect your choice of algorithms.
EDIT:
Here's a thesis of somebody who tried solving a similar problem. It is from 2002, but IMHO it is a good place to start.
您想要考虑深度图。这是最先进的。尝试阅读有关稀疏球面表示的内容。特征面不是很健壮。它对于几个因素并不是一成不变的。最先进的人脸识别使用具有深度信息的 3d PCL。这将使您即使在黑暗中也能认出。您可以使用华硕相机或 Kinect 相机来收集深度信息。希望有帮助！
You want to consider depth maps. That is state of the art. Try to read about sparse spherical representations. Eigen faces is just not very robust. Its not invariant to several factors. State of the art face recognition is using 3d PCL which has depth information. This would allow you to recognize even in the dark. You could use an Asus camera or a Kinect camera to collect depth info. Hope that helps!
哈尔级联在马里莱纳港运行。
Haar 级联是一种读取文件并告诉计算机文件内容的算法。我过去用它来检测脸部、眼镜、微笑、手和可乐罐。
http://en.wikipedia.org/wiki/Haar-like_features
Haar cascade running on a Marilena Port.
A Haar cascade is an algorithm which reads a file and tells the computer what something looks like. I've used it in the past to detect faces, glasses, just a smile, hands and a coke can.
http://en.wikipedia.org/wiki/Haar-like_features