如何让我的电脑检测到我的情绪？ [英] How do I make my computer detect my emotions?
问题描述
我希望我的电脑能够通过网络摄像头了解当我处于某种情绪（自我定义，不仅仅是快乐和悲伤）时我脸部的样子，然后预测我可能的心情（百分之，不是二进制）通过给予培训。比如http://auduno.github.io/clmtrackr/examples/clm_emotiondetection.html，但是为我量身定制，而不是通用的分类器。
我做了一些研究，它似乎我需要使用haar分类器。我看着AForge und Accord，找到了FaceHaarCascade。但这不仅仅是通用的，它也是二元的。它只能检测面部，而不是情绪。至少我需要给它一些面部表情来选择最可能的一个并给出每个的百分比。
你如何用aforge / accord做到这一点？
澄清：我想知道，如何用aforge或者一致创建一个haar分类器，这样我就可以一心情输入1000张图片，1000张图片另一种情绪，让机器学习创建决策树，最后使用它。 Aforge / Accord已提供此*，但此任务没有文档。我还认为眼睛和鼻子对情绪更重要，因为你也可以用假的方式笑（眼睛周围的皱纹紧张）。基本上我会让机器学习决定是什么让我的脸表达某种情感。
* Accord Vision提供HaarCascade和HaarClassifier，而HaarCascade提供了一种名为FromXml的方法，可以从中导入HaarCascades OpenCV XML。为此，我需要创建一个opencv-xml，它引导我访问http://note.sonots.com/SciSoftware/haartraining.html，但是像haartraining -data haarcascade -vec samples.vec -bg negatives.dat -nstages 20 -nsplits 2 -minhitrate 0.999 -maxfalsealarm 0.5 -npos 7000 -nneg 3019 -w 20 -h 20 -nonsym -mem 512 -mode ALL不起作用，这是我被困在那里的地方。编辑：它确实适用于npos<包含的数量。图片。
另外，使用HaarObjectDetector，它不提供阈值输出而是二进制它是对象/它不是对象。我想要的是每种情绪的百分比，即与某些面孔的相似性而不是这是你快乐/不快乐的地方类型的结果。
I want my computer to learn what my face looks like when I am in a certain mood(self defined, not just happy and sad) via webcam and then predict the mood I am likely in(in per cent, not binary) by having given it training. Like http://auduno.github.io/clmtrackr/examples/clm_emotiondetection.html but tailored to me and not a generic classifier.
I did some research and it seems that I need to use a haar classifier. I looked into AForge und Accord, finding FaceHaarCascade. But this is not just generic, it also is binary. And it only detects faces, not emotions. At least I need to give it some facial expressions to choose the most likely one from and give the percentage of each.
How do you do it with aforge/accord?
Clarification: I would like to know, how to create a haar classifier with aforge or accord, so I can input 1000 pictures from me in one mood, 1000 pictures in another mood, let machine-learning create decision trees and finally use it. Aforge/Accord already offers this*, but there is no documentation for this task. I also thought that the eyes and nose are more important for mood as you can also laugh in a fake way(with wrinkles around eyes being tense). Basically I would let machine learning decide what makes my face express a certain emotion.
*Accord Vision offers HaarCascade and HaarClassifier whereas HaarCascade offers a method called FromXml which imports HaarCascades from OpenCV XML. For this I need to create an opencv-xml, which leads me to http://note.sonots.com/SciSoftware/haartraining.html but something like haartraining -data haarcascade -vec samples.vec -bg negatives.dat -nstages 20 -nsplits 2 -minhitrate 0.999 -maxfalsealarm 0.5 -npos 7000 -nneg 3019 -w 20 -h 20 -nonsym -mem 512 -mode ALL does not work, this is where I am stuck there. It does work with npos < number of included pos. pictures.
Also, using the HaarObjectDetector, it does not offer a threshold output but rather a binary It is the object/It is not the object. What I want is % of each mood, ie similarity to certain faces not a "this is where you are happy/unhappy" type of result.
推荐答案
请看我的对这个问题的评论。您可以随时找到关于该主题的一些链接：
http： //www.amazon.com/Unmasking-Face-Recognizing-Emotions-Expressions/dp/1883536367 [ ^ ]，
http://emotion.caltech.edu/papers/Adolphs2002Recognizing.pdf [ ^ ]，
http： //www.cs.ucf.edu/courses/cap6411/lect1012h.PDF [ ^ ]，
http://bit.ly/19htK4F [ ^ ]。
尽管有很多出版物，但我现在非常怀疑。您的意见很可能与众不同。我希望你能认识到这一点：:-)
-SA
Please see my comments to the question. You can always find some links on the topic:
http://www.amazon.com/Unmasking-Face-Recognizing-Emotions-Expressions/dp/1883536367[^],
http://emotion.caltech.edu/papers/Adolphs2002Recognizing.pdf[^],
http://www.cs.ucf.edu/courses/cap6411/lect1012h.PDF[^],
http://bit.ly/19htK4F[^].
Despite of plenty of publications, I'm pretty much skeptical at this time. It's likely that your opinion is different. I hope you can recognize this: :-)
—SA
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