开始

今天给大家推荐一个效果很好速度又快(逃))的人脸质量评价库，这个人脸质量评价库是我自己训的，为了方便表示，我给它起个名字，就BFQ(很像BBQ).

其实网上有很多开源的人脸质量算法，其中的很多甚至开源了模型出来，这给需要用人脸质量评价算法来过滤人脸的人们提供了很好的工具，但是很多脸质量算法的效果并不好，或是不能满足自己的需要，或是只能满足一部分的需求，所以我们往往需要多个质量算法库联合去判断一个人脸的质量。

前言

故事(事故)起源于我老大最近跟我说的一个脑洞，他说他最近看了Advhat 的相关介绍，说能不能在活体识别上面尝试一下Advhat, 仿照Advhat做一个特殊贴纸，贴到一张假脸的某个部位，然后就用这样的一张贴了特殊纸张的假脸攻破活体识别系统．

我当时觉得应该不太可能，毕竟活体任务和人脸识别任务不同，活体识别任务学的就是真人和非真人介质的不同，无论生成什么样的纸张，毕竟逃离不了纸张透过摄像头的后的独特介质信息，况且即使生成了特殊的贴纸，然后再打印出来，然后再通过摄像头去识别，这样这张生成的’数字’贴纸就经过了两次失真，应该是没啥效果的．

我就把我的观点和老大一说，老大说还是让我尝试一下．于是我就做个实验验证一下，没想到我被实验结果深深的打脸．．．

Let’s do it!

If you use your face to pay a sum of money by ALIPAY, you may find that it sometimes requires you to do some facial movement to check whether you are a real person or not.

So as you see, facial motion detection is actually being used in many circumstances. You may want to figure out how to detect facial motion.

In this post, I will take motion blink as a example to demonstrate how to do realize it.

So Let’s do it.

Is Face Antispoofing only a binary classification task?

Of course, we can consider face antispoofing as a binary classification task. We can train a classifier to distinguish a face image between liveness and fake.

It may work well but it can not fully use all information of the input face image. In order to push the limit of our trained data and trained classifier, we have to cultivate other information that could help us to better discriminate an attack face.

How dose people spoof a digital device with face recognition

To attack a image recognition system is easy. Even to change a single pixel could successfully let the recognition system lose efficiency. So people would use the loophole to simulate a set of fake images to spoof the face recognition system.

So in order to defense the spoofing, we should simulate all the possible attacking conditions in the real life with limited training dataset.

Align or not, Texture or shape

Relationship with Face Recognition(FR)

The aim of Face Recognition is to shorten the distance of one person from different scene, even from different medium. For example, FR should shorten the distance between the person in real life and the person’s picture in paper or tv or phone.

However, Face Antispoofing(FA) just did the opposite thing.

FA should shorten the person from the same medium. FA should shroten the distance between the two different real persons. And FA should widen the distance of the people from different medium, even the two persons are the same.
So as we can see, to differentiate medium is the key of FA.