特征脸算法
我正在使用 OpenCV 编写人脸识别程序。
当生成特征脸时：
- 我需要使用未知面孔的大型数据库吗？
- 我是否只需要使用我希望系统识别的人的照片？
- 我需要同时使用两者吗？
我说的是特征脸生成，这是“学习”步骤。
我需要使用多少张照片才能获得不错的准确性？更像是20，还是2000？
谢谢
I am programming a face recognition program using OpenCV.
When generating the eigenfaces:
- do I need to use a big database of unknown faces ?
- do I need to use only photos of the people I want my system to recognize ?
- do I need to use both ?
I am talking about the eigenfaces generation, this is the "learning" step.
And how many photos do I need to use to have decent accuracy ? More like 20, or 2000 ?
Thanks
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特征脸的工作原理是使用主成分分析或 PCA 将面部投影到特定的“面部基础”中。该基础不必包含您想认识的人的照片。
相反，我鼓励您基于注册良好的大数据库（至少 10k 个面孔）进行训练（特征脸不适用于移动的图像）。 Turk 和 Pentland 的原始论文之所以引人注目，部分原因在于他们发布了大型 PIN 注册人脸数据库。我还想说，尝试将数据库和测试输入之间的照明标准化为相同。
在测试方面，前 20 个组件应足以重建人类可识别的面部，前 100 个组件应足以区分基本上任意大数据集的任何两个面部。
Eigenfaces works by projecting the faces into a particular "face basis" using principal component analysis or PCA. The basis does not have to include photos of people you want to recognize.
Instead, I would encourage you to train based upon a big database (at least 10k faces) that is well registered (eigenfaces doesn't work well with images that are shifted). The original paper by Turk and Pentland was remarkable partly due to the large pin registered face database they released. I would also say that try to have the lighting normalized to the same between the database and your test inputs.
In terms of testing, first 20 components should be sufficient to reconstruct a human recognizable face and first 100 components should be enough to discriminate between any two face for essentially arbitrarily large dataset.
你不需要太多的随机面孔来组成一张人脸；接近 20 的值应该会产生良好的结果，如果可以的话，也许可以使用更多。它们应该尽可能地彼此排列，正面，并且在相同的照明条件下拍摄灰度照片。
You don't need too many random faces to compose a human face; somewhere close to 20 should give good results, maybe go with more if you can. They should all be lined up as much as possible to one another, front facing, and photos in grayscale under the same lighting conditions.