blocks|key|4168379|text|那Eigenfaces呢？利用简单的数学来存储人脸的可识别特征向量，并使用多个向量重建人脸。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|4168380|代码在Python和here中都可以找到。|4168381|entityMap|0|LINK|mutability|MUTABLE|url|http://www.owlnet.rice.edu/~elec301/Projects99/faces/index.html|1|http://www.owlnet.rice.edu/~elec301/Projects99/faces/code.html^0|1|A|0|0|A|4|1|0^^$0|@$1|2|3|4|5|6|7|P|8|@]|9|@$A|Q|B|R|1|S]]|C|$]]|$1|D|3|E|5|6|7|T|8|@]|9|@$A|U|B|V|1|W]]|C|$]]|$1|F|3|-4|5|6|7|X|8|@]|9|@]|C|$]]]|G|$H|$5|I|J|K|C|$L|M]]|N|$5|I|J|K|C|$L|O]]]]

How about <a href="http://www.owlnet.rice.edu/~elec301/Projects99/faces/index.html" rel="noreferrer">Eigenfaces</a>? Utilizes simple mathematics to store recognizable eigenvector of the face and reconstruct faces using multiple vectors.

The code is all available in Python as well <a href="http://www.owlnet.rice.edu/~elec301/Projects99/faces/code.html" rel="noreferrer">here</a>.

blocks|key|3322775|text|OpenCV是一个很棒的计算机视觉库。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|3322776|这是OpenCV+wiki+page+on+face+recognition。|3322777|entityMap|0|LINK|mutability|MUTABLE|url|http://opencv.willowgarage.com/wiki/|1|http://opencv.willowgarage.com/wiki/FaceRecognition^0|0|6|0|0|2|10|1|0^^$0|@$1|2|3|4|5|6|7|P|8|@]|9|@$A|Q|B|R|1|S]]|C|$]]|$1|D|3|E|5|6|7|T|8|@]|9|@$A|U|B|V|1|W]]|C|$]]|$1|F|3|-4|5|6|7|X|8|@]|9|@]|C|$]]]|G|$H|$5|I|J|K|C|$L|M]]|N|$5|I|J|K|C|$L|O]]]]

<a href="http://opencv.willowgarage.com/wiki/" rel="nofollow noreferrer">OpenCV</a> is a great computer vision library.

Here's the <a href="http://opencv.willowgarage.com/wiki/FaceRecognition" rel="nofollow noreferrer">OpenCV wiki page on face recognition</a>.

blocks|key|3322819|text|除了特征脸之外，我还会看看Fisherfaces。这是一篇学术论文，比较了两种算法Eigenfaces+vs.+Fisherfaces的性能。它显示了与Fisherfaces更好的性能。我也同意tfinniga的观点，OpenCV值得你花时间，我以前也用过它进行人脸检测。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|3322820|最后，你应该更具体一些。是否要检测图片中存在人脸的时间，然后确定要检测图片中的特定人脸的位置？这里列出的解决方案是针对后一个问题的。如果你想解决前一个问题，我建议在文献中搜索adaboost和haar的特征。|3322821|entityMap|0|LINK|mutability|MUTABLE|url|http://www1.cs.columbia.edu/~belhumeur/journal/fisherface-pami97.pdf^0|15|Q|0|0|0^^$0|@$1|2|3|4|5|6|7|N|8|@]|9|@$A|O|B|P|1|Q]]|C|$]]|$1|D|3|E|5|6|7|R|8|@]|9|@]|C|$]]|$1|F|3|-4|5|6|7|S|8|@]|9|@]|C|$]]]|G|$H|$5|I|J|K|C|$L|M]]]]

In addition to Eigenfaces, I would look at Fisherfaces. Here is an academic paper that compares the performance of both algorithms <a href="http://www1.cs.columbia.edu/~belhumeur/journal/fisherface-pami97.pdf" rel="noreferrer">Eigenfaces vs. Fisherfaces</a>. It shows better performance with Fisherfaces. I also agree with tfinniga that OpenCV is worth your time, I've used it before for face detection.

Finally you should be more specific. Do you want to detect when there is a face in a picture and then identify where or do you want to detect a specific face in a picture? The solutions listed here are for the latter question. If you want to tackle the former question I suggest searching the literature for adaboost and haar features.

blocks|key|914746|text|你可能找不到太多完整的人脸识别软件。如果你想这样做，你最好的机会就是实现某人的论文中的内容。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|914747|大约4年前，我相信CMU的某个人写了一个算法，这是我见过的最成功的人脸识别算法……我会试着帮你找到它，除非别人知道我在说什么。|914748|如果您以前从未实现过计算机视觉代码，那么您可以通过将其分解为更简单的问题来解决该问题。你要找多少人？如果是一小群人，颜色空间识别算法能起作用吗？如果人们有不同的肤色和不同的头发颜色，那么你可能只需要使用基本的颜色空间识别就可以得到一个好的结果。|914749|否则，你将面临一个大项目。|914750|编辑：here's+a+list+of+some+CMU+articles+that+may+point+you+in+the+right+direction|offset|length|914751|如果我是你，我会阅读其中的一些文章，并跟进参考资料。|914752|entityMap|0|LINK|mutability|MUTABLE|url|http://www.ri.cmu.edu/research_center_detail.html?type=publications%25C2%25A2er_id=4&menu_id=262^0|0|0|0|0|3|24|0|0|0^^$0|@$1|2|3|4|5|6|7|V|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|W|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|X|8|@]|9|@]|A|$]]|$1|F|3|G|5|6|7|Y|8|@]|9|@]|A|$]]|$1|H|3|I|5|6|7|Z|8|@]|9|@$J|10|K|11|1|12]]|A|$]]|$1|L|3|M|5|6|7|13|8|@]|9|@]|A|$]]|$1|N|3|-4|5|6|7|14|8|@]|9|@]|A|$]]]|O|$P|$5|Q|R|S|A|$T|U]]]]

You're probably not going to find much finished software for face recognition. If you want to do it, your best chance is to implement something that is in someone's thesis.

About 4 years ago, someone at CMU, I believe, wrote an algorithm that was the most successful face recognition algorithm I have ever seen... I will try to find it for you, unless someone else knows what I am talking about.

If you've never implemented computer vision code before, then you might be able to tackle the problem by breaking it down into a simpler problem. How many people are you looking for? If it's a small group of people, could a color-space recognition algorithm work? If the people have differing skin tones, and differing hair colors, then you might just be able to use basic color-space recognition to get a good result.

Otherwise, you are in for a big project.

EDIT: <a href="http://www.ri.cmu.edu/research_center_detail.html?type=publications&amp;center_id=4&amp;menu_id=262" rel="nofollow noreferrer">here's a list of some CMU articles that may point you in the right direction</a>

If I were you, I would read some of those articles, and follow up on the references.

blocks|key|104275|text|这类任务通常使用神经网络来完成。这是你寻找的主要方向。还有一些更具体的网络，你可以看看Kohonen神经网络。它是最简单的图像识别网络，也可以用于人脸识别任务。只使用这一种网络，但是将它们中的几个连接在一起，你不会得到任何好的结果。我不能给出更多细节，因为我只在理论上知道这一点，但在这一领域没有实践技能。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|104276|顺便说一句，我认为你不会找到任何库来完成这项任务，如果你有这样的库，为什么不做一个产品并出售它呢？|104277|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|F|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|G|8|@]|9|@]|A|$]]|$1|D|3|-4|5|6|7|H|8|@]|9|@]|A|$]]]|E|$]]

This kind of task is usually done using Neural Networks. This is your primary direction to look. 
Also some more specific kind of network for you to look is Kohonen Neural Network. It is the simpliest Image Recognition network, that can be also used in a face recognition tasks. You won't achieve any good results using ONLY this one kind of net, but connecting few of them together. I can't give more detail, as I know this only in theory, but have no practical skills in this area.

Btw, I don't think you will find any libraries for that task, as usually if you have such library - why dont make a product and sell it?

blocks|key|4168449|text|There's+a+whole+raft+of+them+over+at+CodeProject+and+in+different+languages+too.|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|4168450|entityMap|0|LINK|mutability|MUTABLE|url|http://www.codeproject.com/info/search.aspx?q=face%2Brecognition^0|0|28|0|0^^$0|@$1|2|3|4|5|6|7|L|8|@]|9|@$A|M|B|N|1|O]]|C|$]]|$1|D|3|-4|5|6|7|P|8|@]|9|@]|C|$]]]|E|$F|$5|G|H|I|C|$J|K]]]]

<a href="http://www.codeproject.com/info/search.aspx?q=face+recognition" rel="nofollow noreferrer">There's a whole raft of them over at CodeProject and in different languages too.</a>

blocks|key|104448|text|我写了一个完整的教程和演示程序，用免费源代码从网络摄像头实时执行人脸检测和人脸识别(基于OpenCV中的特征脸)：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|104449|http://www.shervinemami.co.cc/faceRecognition.html|offset|length|104450|entityMap|0|LINK|mutability|MUTABLE|url^0|0|0|1E|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|N|8|@]|9|@$D|O|E|P|1|Q]]|A|$]]|$1|F|3|-4|5|6|7|R|8|@]|9|@]|A|$]]]|G|$H|$5|I|J|K|A|$L|C]]]]

I wrote a whole tutorial and demo program with free source code for performing face detection and face recognition in real-time from a webcam (based on Eigenfaces in OpenCV):

<a href="http://www.shervinemami.co.cc/faceRecognition.html" rel="nofollow">http://www.shervinemami.co.cc/faceRecognition.html</a>

blocks|key|3322899|text|我发现Luxand+Facesdk在人脸识别和识别方面是最好的。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|3322900|entityMap|0|LINK|mutability|MUTABLE|url|http://www.luxand.com/^0|3|E|0|0^^$0|@$1|2|3|4|5|6|7|L|8|@]|9|@$A|M|B|N|1|O]]|C|$]]|$1|D|3|-4|5|6|7|P|8|@]|9|@]|C|$]]]|E|$F|$5|G|H|I|C|$J|K]]]]

I find <a href="http://www.luxand.com/" rel="nofollow">Luxand Facesdk</a> the best for Face recognition and identification.

Can anyone point me in the right direction of some facial recognition libraries &amp; algorithms ? I've tried searching/googling but i mostly find thesises and very little real software.

Facial recognition/merging software

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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