关于ADABOOST人脸检测负样本的选取

其他人脸检测方法，负样本需要的数量我不清楚，但是ADABOOST所需负样本数量非常之大。在训练层数后期，当最大误警率很低，如2*10-5次方，同时参与训练的负样本为4000时，那么需要的负样本就要2*10^8。解决的方法是自举。但自举最重要的是负样本本身的尺寸一定要足够大1000*1000。同时针对不同应用场景，我们需要不同的负样本。比如车内的人脸检测，负样本本身就要体现出车内分人脸的特性，湖水 蓝天之类的就没有任何区分效果。这在下面的Q&A中也有体现。

Viola-Jones‘ AdaBoost method is very popular for face detection? We need lots of positive and negative samples o train a face detector.

The rule for collecting positive sample is simple: the image which contains faces. But the rule for collecting negative sample is not very clear: the image which does not contains faces.

But there are so many scene that do not contain faces (which may be sky, river, house animals etc.). Which should I collect it? How can know I have collected enough negative samples?

Some suggested idea for negative samples: using the positive samples and crop the face region using the left part as negative samples. Is this work?

Answer:

You have asked many questions inside your thread.

1. Amount of samples. As a rule of thumbs: When you train a detector you need roughly few thousands positive and negative examples per stage. Typical detector has 10-20 stages. Each stage reduces the amount of negative by a factor of 2. So
   you will need roughly 3,000 - 10,000 positive examples and ~5,000,000 to 100,000,000 negative examples.
2. Which negatives to take. A rule of thumb: You need to find a face in a given environment. So you need to take that environment as negative examples. For instance, if you try to detect faces of students sitting in a classroom than take as
   negative examples images from the classroom (walls, windows, human body, clothes etc). Taking images of the moon or of the sky will probably not help you. If you don‘t know your environment than just take as much as possible different natural images (under
   different light conditions).
3. Should you take facial parts (like an eye, or a nose) as negative? You can but this is definitely not enough (to take only those negatives). The real strength of the detector will come from the negative images which represent the typical
   background of the faces
4. How to collect/generate negative samples - You don‘t actually need many negative images. You can take 1000 images and generate 10,000,000 negative samples from them. Here is how you do it. Suppose you take a photo of a car of 1 mega pixel
   resolution 1000x1000 pixels. Suppose than you want to train face detector to work on resolution of 20x20 pixels (like openCV did). So you take your 1000x1000 big image and cut it to pieces of 20x20. You can get 2,500 pieces (50x50). So this is how from a single
   big image you generated 2,500 negative examples. Now you can take the same big image and cut it to pieces of size 10x10 pixels. You will now have additional 10,000 negative examples. Each example is of size 10x10 pixels and you can enlarge it by factor of
   2 to force all the sample to have the same size. You can repeat this process as much as you want (cutting the input image to pieces of different size). Mathematically speaking, if your image is of size NxN - You can generate O(N^4) negative examples from it
   by taking each possible rectangle inside it.
5. In step 4, I described how to take a single big image and cut it to a large amount of negative examples. I must warn you that negative examples should not have high co-variance so I don‘t recommend taking only one image and generating 1 million negative
   examples from it. As a rule of thumb - create a library of 1000 images (or download random images from Google). Verify than none of the images contains faces. Crop about 10,000 negative examples from each image and now you have got a decent 10,000,000 negative
   examples. Train your detector. In the next step you can cut each image to ~50,000 (partially overlapping pieces) and thus enlarge your amount of negatives to 50 millions. You will start having very good results with it.
6. Final enhancement step of the detector. When you already have a rather good detector, run it on many images. It will produce false detections (detect face where there is no face). Gather all those false detections and add them to your negative
   set. Now retrain the detector once again. The more such iterations you do the better your detector becomes
7. Real numbers - The best face detectors today (like Facebooks) use hundreds of millions of positive examples and billions of negatives. As positive examples they take not only frontal faces but faces in many orientations, different facial
   expressions (smiling, shouting, angry,...), different age groups, different genders, different races (Caucasians, blacks, Thai, Chinese,....), with or without glasses/hat/sunglasses/make-up etc. You will not be able to compete with the best, so don‘t get angry
   if your detector misses some faces.

   Good luck