Haar级联分类器概述(An overview of Haar cascade classifier)

Haar cascade classifier is an open cv algorithm. It makes classification between images with an object ( i.e face) and images without an object (i.e with non-faces).

Haar级联分类器是一种开放式简历算法。 它可以在带有对象(即人脸)的图像和没有对象(即非人脸)的图像之间进行分类。

Initially, several hundreds of images with face and several hundreds of images with non-faces have been given to this classifier. This classifier was then trained by applying machine learning methods like the neural networks to recognize human faces. It then extracted Haar Features from those images and stored them in an xml file.

最初，已经给该分类器提供了数百张带有面部的图像和几百张具有非面部的图像。 然后通过应用机器学习方法(例如神经网络)识别人脸来训练该分类器。 然后从这些图像中提取Haar功能，并将其存储在xml文件中。

什么是Haar功能以及如何提取这些功能？ (What are Haar Features and how these features are extracted?)

Basically for face detection, the classifier looks for the most relevant features on the face such as eyes, nose, lips, forehead, eyebrows because we know that although people have different looks, these features are in the similar positions on the face.

基本上脸détection，分类查找该面对的最相关的特征，如眼睛，鼻子，嘴唇，额头，眉毛，因为我们知道，虽然人们有不同的外观，这些功能都在面对相似的立场。

Haar features are white and black pixels on the face. Also, we know that the greyscale image of a face does not have completely white and black pixels but here we are considering an ideal case where white pixels are lighter pixels and black pixels are darker pixels.

Haar特征是脸上的白色和黑色像素。 同样，我们知道人脸的灰度图像不具有完全的白色和黑色像素，但是在这里我们考虑的理想情况是白色像素是较亮的像素，黑色像素是较暗的像素。

In the above figure, eyebrow contains darker pixels and forehead contains lighter pixels. Similarly, it goes for eyes, nose and lips.

在上图中，眉毛包含较暗的像素，前额包含较亮的像素。 同样，它适用于眼睛，鼻子和嘴唇。

比较如何完成 (How comparison is done)

Now if a new input image is given to the classifier, it compares the Haar Features from the xml file and applies it to the input image. If it passes through all the stages of haar feature comparison, then it’s a face, else not.

现在，如果将新的输入图像提供给分类器，它将比较xml文件中的Haar功能并将其应用于输入图像。 如果它经过了haar特征比较的所有阶段，那么它就是一张面Kong，否则就不是。