基于Openpose框架的人脸关键点heatmaps预测模型设计、训练_heatmap openpose

    众所周知，人脸识别中有一项重要的任务-人脸关键点预测，通过这个环节对齐，才能进行人脸识别，提高人脸识别的准确率。另外，一些活体检测/人脸状态分析也需要利用该方案进行实现。

    经典的人脸检测模型MTCNN中具有人脸关键点的预测功能，但其关键预测精度比较差，对于大角度、模糊、遮挡、小尺度等情况的人脸效果下降更加严重。因此我结合openpose的关键点预测模型，自行设计了如下的人脸关键点热图预测模型，经过验证可以很好的实现人脸关键点预测的效果。

训练网络设计

    由于经典的openpose框架中heatmaps的热图有6个stage进行预测和中间监督，但我按照原始的框架发现到达后面几个stage后，loss基本和前面的stage一致，但多个stage对模型的速度会有影响，因此设计了3个stage和4个stage的版本，这里主要介绍4个stage的版本。

利用netscope可以将设计的网络可视化如下（如果看不清，可以利用我下面提供的proto内容自行验证）：

所设计的网络proto文件内容如下：

name: "landmarks-net"
 
input: "data"
input_shape {
  dim: 1
  dim: 3
  dim: 112
  dim: 112
}
 
layer {
  name: "conv1_1"
  type: "Convolution"
  bottom: "data"
  top: "conv1_1"
  param {
    lr_mult: 1.0
    decay_mult: 1
  }
  param {
    lr_mult: 2.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu1_1"
  type: "ReLU"
  bottom: "conv1_1"
  top: "conv1_1"
}
layer {
  name: "conv1_2"
  type: "Convolution"
  bottom: "conv1_1"
  top: "conv1_2"
  param {
    lr_mult: 1.0
    decay_mult: 1
  }
  param {
    lr_mult: 2.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu1_2"
  type: "ReLU"
  bottom: "conv1_2"
  top: "conv1_2"
}
layer {
  name: "pool1_stage1"
  type: "Pooling"
  bottom: "conv1_2"
  top: "pool1_stage1"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "conv2_1"
  type: "Convolution"
  bottom: "pool1_stage1"
  top: "conv2_1"
  param {
    lr_mult: 1.0
    decay_mult: 1
  }
  param {
    lr_mult: 2.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu2_1"
  type: "ReLU"
  bottom: "conv2_1"
  top: "conv2_1"
}
layer {
  name: "conv2_2"
  type: "Convolution"
  bottom: "conv2_1"
  top: "conv2_2"
  param {
    lr_mult: 1.0
    decay_mult: 1
  }
  param {
    lr_mult: 2.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu2_2"
  type: "ReLU"
  bottom: "conv2_2"
  top: "conv2_2"
}
layer {
  name: "pool2_stage1"
  type: "Pooling"
  bottom: "conv2_2"
  top: "pool2_stage1"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "conv3_1"
  type: "Convolution"
  bottom: "pool2_stage1"
  top: "conv3_1"
  param {
    lr_mult: 1.0
    decay_mult: 1
  }
  param {
    lr_mult: 2.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu3_1"
  type: "ReLU"
  bottom: "conv3_1"
  top: "conv3_1"
}
layer {
  name: "conv3_2"
  type: "Convolution"
  bottom: "conv3_1"
  top: "conv3_2"
  param {
    lr_mult: 1.0
    decay_mult: 1
  }
  param {
    lr_mult: 2.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu3_2"
  type: "ReLU"
  bottom: "conv3_2"
  top: "conv3_2"
}
layer {
  name: "conv3_3"
  type: "Convolution"
  bottom: "conv3_2"
  top: "conv3_3"
  param {
    lr_mult: 1.0
    decay_mult: 1
  }
  param {
    lr_mult: 2.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu3_3"
  type: "ReLU"
  bottom: "conv3_3"
  top: "conv3_3"
}
layer {
  name: "conv3_4"
  type: