# 3.2 特征点检测（Landmark detection）

仅对目标的关键特征点坐标进行定位，这些关键点被称为landmarks

[![](/files/-Le0cdWn1DtsbR6IywTB)](https://github.com/fengdu78/deeplearning_ai_books/blob/master/images/100b265aefc4b0170fb39ed339e5181a.png)

选定特征点个数，并生成包含特征点的标签训练集，利用神经网络输出脸部关键特征点的位置

具体做法:准备一个卷积网络和一些特征集，将人脸图片输入卷积网络，输出1表示有人脸，0表示没有人脸，然后输出（$$l\_{1x}$$，$$l\_{1y}$$）……直到（$$l\_{64x}$$，$$l\_{64y}$$），$$l$$代表一个特征，即该网络模型共检测人脸上64处特征点，加上是否为face的标志位，输出label共有64x2+1=129个值，即有129个输出单元，由此实现对图片的人脸检测和定位

检测人体姿势动作：

![](/files/-Le0cdWpSlcUrqVgbUd7)

特征点的特性在所有图片中必须保持一致


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