Merge branch 'release/debian/0.32.1-1'debian/0.32.1-1

11 files changed, 56425 insertions, 0 deletions

diff --git a/subprojects/shotwell-facedetect/deploy.prototxt b/subprojects/shotwell-facedetect/deploy.prototxt
new file mode 100644
index 0000000..a128515
--- /dev/null
+++ b/subprojects/shotwell-facedetect/deploy.prototxt
@@ -0,0 +1,1790 @@
+input: "data"
+input_shape {
+  dim: 1
+  dim: 3
+  dim: 300
+  dim: 300
+}
+
+layer {
+  name: "data_bn"
+  type: "BatchNorm"
+  bottom: "data"
+  top: "data_bn"
+  param {
+    lr_mult: 0.0
+  }
+  param {
+    lr_mult: 0.0
+  }
+  param {
+    lr_mult: 0.0
+  }
+}
+layer {
+  name: "data_scale"
+  type: "Scale"
+  bottom: "data_bn"
+  top: "data_bn"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 1.0
+  }
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "conv1_h"
+  type: "Convolution"
+  bottom: "data_bn"
+  top: "conv1_h"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 32
+    pad: 3
+    kernel_size: 7
+    stride: 2
+    weight_filler {
+      type: "msra"
+      variance_norm: FAN_OUT
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "conv1_bn_h"
+  type: "BatchNorm"
+  bottom: "conv1_h"
+  top: "conv1_h"
+  param {
+    lr_mult: 0.0
+  }
+  param {
+    lr_mult: 0.0
+  }
+  param {
+    lr_mult: 0.0
+  }
+}
+layer {
+  name: "conv1_scale_h"
+  type: "Scale"
+  bottom: "conv1_h"
+  top: "conv1_h"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 1.0
+  }
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "conv1_relu"
+  type: "ReLU"
+  bottom: "conv1_h"
+  top: "conv1_h"
+}
+layer {
+  name: "conv1_pool"
+  type: "Pooling"
+  bottom: "conv1_h"
+  top: "conv1_pool"
+  pooling_param {
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "layer_64_1_conv1_h"
+  type: "Convolution"
+  bottom: "conv1_pool"
+  top: "layer_64_1_conv1_h"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 32
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "layer_64_1_bn2_h"
+  type: "BatchNorm"
+  bottom: "layer_64_1_conv1_h"
+  top: "layer_64_1_conv1_h"
+  param {
+    lr_mult: 0.0
+  }
+  param {
+    lr_mult: 0.0
+  }
+  param {
+    lr_mult: 0.0
+  }
+}
+layer {
+  name: "layer_64_1_scale2_h"
+  type: "Scale"
+  bottom: "layer_64_1_conv1_h"
+  top: "layer_64_1_conv1_h"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 1.0
+  }
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "layer_64_1_relu2"
+  type: "ReLU"
+  bottom: "layer_64_1_conv1_h"
+  top: "layer_64_1_conv1_h"
+}
+layer {
+  name: "layer_64_1_conv2_h"
+  type: "Convolution"
+  bottom: "layer_64_1_conv1_h"
+  top: "layer_64_1_conv2_h"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 32
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "layer_64_1_sum"
+  type: "Eltwise"
+  bottom: "layer_64_1_conv2_h"
+  bottom: "conv1_pool"
+  top: "layer_64_1_sum"
+}
+layer {
+  name: "layer_128_1_bn1_h"
+  type: "BatchNorm"
+  bottom: "layer_64_1_sum"
+  top: "layer_128_1_bn1_h"
+  param {
+    lr_mult: 0.0
+  }
+  param {
+    lr_mult: 0.0
+  }
+  param {
+    lr_mult: 0.0
+  }
+}
+layer {
+  name: "layer_128_1_scale1_h"
+  type: "Scale"
+  bottom: "layer_128_1_bn1_h"
+  top: "layer_128_1_bn1_h"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 1.0
+  }
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "layer_128_1_relu1"
+  type: "ReLU"
+  bottom: "layer_128_1_bn1_h"
+  top: "layer_128_1_bn1_h"
+}
+layer {
+  name: "layer_128_1_conv1_h"
+  type: "Convolution"
+  bottom: "layer_128_1_bn1_h"
+  top: "layer_128_1_conv1_h"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+    stride: 2
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "layer_128_1_bn2"
+  type: "BatchNorm"
+  bottom: "layer_128_1_conv1_h"
+  top: "layer_128_1_conv1_h"
+  param {
+    lr_mult: 0.0
+  }
+  param {
+    lr_mult: 0.0
+  }
+  param {
+    lr_mult: 0.0
+  }
+}
+layer {
+  name: "layer_128_1_scale2"
+  type: "Scale"
+  bottom: "layer_128_1_conv1_h"
+  top: "layer_128_1_conv1_h"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 1.0
+  }
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "layer_128_1_relu2"
+  type: "ReLU"
+  bottom: "layer_128_1_conv1_h"
+  top: "layer_128_1_conv1_h"
+}
+layer {
+  name: "layer_128_1_conv2"
+  type: "Convolution"
+  bottom: "layer_128_1_conv1_h"
+  top: "layer_128_1_conv2"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "layer_128_1_conv_expand_h"
+  type: "Convolution"
+  bottom: "layer_128_1_bn1_h"
+  top: "layer_128_1_conv_expand_h"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    pad: 0
+    kernel_size: 1
+    stride: 2
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "layer_128_1_sum"
+  type: "Eltwise"
+  bottom: "layer_128_1_conv2"
+  bottom: "layer_128_1_conv_expand_h"
+  top: "layer_128_1_sum"
+}
+layer {
+  name: "layer_256_1_bn1"
+  type: "BatchNorm"
+  bottom: "layer_128_1_sum"
+  top: "layer_256_1_bn1"
+  param {
+    lr_mult: 0.0
+  }
+  param {
+    lr_mult: 0.0
+  }
+  param {
+    lr_mult: 0.0
+  }
+}
+layer {
+  name: "layer_256_1_scale1"
+  type: "Scale"
+  bottom: "layer_256_1_bn1"
+  top: "layer_256_1_bn1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 1.0
+  }
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "layer_256_1_relu1"
+  type: "ReLU"
+  bottom: "layer_256_1_bn1"
+  top: "layer_256_1_bn1"
+}
+layer {
+  name: "layer_256_1_conv1"
+  type: "Convolution"
+  bottom: "layer_256_1_bn1"
+  top: "layer_256_1_conv1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 256
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+    stride: 2
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "layer_256_1_bn2"
+  type: "BatchNorm"
+  bottom: "layer_256_1_conv1"
+  top: "layer_256_1_conv1"
+  param {
+    lr_mult: 0.0
+  }
+  param {
+    lr_mult: 0.0
+  }
+  param {
+    lr_mult: 0.0
+  }
+}
+layer {
+  name: "layer_256_1_scale2"
+  type: "Scale"
+  bottom: "layer_256_1_conv1"
+  top: "layer_256_1_conv1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 1.0
+  }
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "layer_256_1_relu2"
+  type: "ReLU"
+  bottom: "layer_256_1_conv1"
+  top: "layer_256_1_conv1"
+}
+layer {
+  name: "layer_256_1_conv2"
+  type: "Convolution"
+  bottom: "layer_256_1_conv1"
+  top: "layer_256_1_conv2"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 256
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "layer_256_1_conv_expand"
+  type: "Convolution"
+  bottom: "layer_256_1_bn1"
+  top: "layer_256_1_conv_expand"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 256
+    bias_term: false
+    pad: 0
+    kernel_size: 1
+    stride: 2
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "layer_256_1_sum"
+  type: "Eltwise"
+  bottom: "layer_256_1_conv2"
+  bottom: "layer_256_1_conv_expand"
+  top: "layer_256_1_sum"
+}
+layer {
+  name: "layer_512_1_bn1"
+  type: "BatchNorm"
+  bottom: "layer_256_1_sum"
+  top: "layer_512_1_bn1"
+  param {
+    lr_mult: 0.0
+  }
+  param {
+    lr_mult: 0.0
+  }
+  param {
+    lr_mult: 0.0
+  }
+}
+layer {
+  name: "layer_512_1_scale1"
+  type: "Scale"
+  bottom: "layer_512_1_bn1"
+  top: "layer_512_1_bn1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 1.0
+  }
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "layer_512_1_relu1"
+  type: "ReLU"
+  bottom: "layer_512_1_bn1"
+  top: "layer_512_1_bn1"
+}
+layer {
+  name: "layer_512_1_conv1_h"
+  type: "Convolution"
+  bottom: "layer_512_1_bn1"
+  top: "layer_512_1_conv1_h"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+    stride: 1 # 2
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "layer_512_1_bn2_h"
+  type: "BatchNorm"
+  bottom: "layer_512_1_conv1_h"
+  top: "layer_512_1_conv1_h"
+  param {
+    lr_mult: 0.0
+  }
+  param {
+    lr_mult: 0.0
+  }
+  param {
+    lr_mult: 0.0
+  }
+}
+layer {
+  name: "layer_512_1_scale2_h"
+  type: "Scale"
+  bottom: "layer_512_1_conv1_h"
+  top: "layer_512_1_conv1_h"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 1.0
+  }
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "layer_512_1_relu2"
+  type: "ReLU"
+  bottom: "layer_512_1_conv1_h"
+  top: "layer_512_1_conv1_h"
+}
+layer {
+  name: "layer_512_1_conv2_h"
+  type: "Convolution"
+  bottom: "layer_512_1_conv1_h"
+  top: "layer_512_1_conv2_h"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 256
+    bias_term: false
+    pad: 2 # 1
+    kernel_size: 3
+    stride: 1
+    dilation: 2
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "layer_512_1_conv_expand_h"
+  type: "Convolution"
+  bottom: "layer_512_1_bn1"
+  top: "layer_512_1_conv_expand_h"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  convolution_param {
+    num_output: 256
+    bias_term: false
+    pad: 0
+    kernel_size: 1
+    stride: 1 # 2
+    weight_filler {
+      type: "msra"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.0
+    }
+  }
+}
+layer {
+  name: "layer_512_1_sum"
+  type: "Eltwise"
+  bottom: "layer_512_1_conv2_h"
+  bottom: "layer_512_1_conv_expand_h"
+  top: "layer_512_1_sum"
+}
+layer {
+  name: "last_bn_h"
+  type: "BatchNorm"
+  bottom: "layer_512_1_sum"
+  top: "layer_512_1_sum"
+  param {
+    lr_mult: 0.0
+  }
+  param {
+    lr_mult: 0.0
+  }
+  param {
+    lr_mult: 0.0
+  }
+}
+layer {
+  name: "last_scale_h"
+  type: "Scale"
+  bottom: "layer_512_1_sum"
+  top: "layer_512_1_sum"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1.0
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 1.0
+  }
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "last_relu"
+  type: "ReLU"
+  bottom: "layer_512_1_sum"
+  top: "fc7"
+}
+
+layer {
+  name: "conv6_1_h"
+  type: "Convolution"
+  bottom: "fc7"
+  top: "conv6_1_h"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "conv6_1_relu"
+  type: "ReLU"
+  bottom: "conv6_1_h"
+  top: "conv6_1_h"
+}
+layer {
+  name: "conv6_2_h"
+  type: "Convolution"
+  bottom: "conv6_1_h"
+  top: "conv6_2_h"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    stride: 2
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "conv6_2_relu"
+  type: "ReLU"
+  bottom: "conv6_2_h"
+  top: "conv6_2_h"
+}
+layer {
+  name: "conv7_1_h"
+  type: "Convolution"
+  bottom: "conv6_2_h"
+  top: "conv7_1_h"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "conv7_1_relu"
+  type: "ReLU"
+  bottom: "conv7_1_h"
+  top: "conv7_1_h"
+}
+layer {
+  name: "conv7_2_h"
+  type: "Convolution"
+  bottom: "conv7_1_h"
+  top: "conv7_2_h"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    stride: 2
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "conv7_2_relu"
+  type: "ReLU"
+  bottom: "conv7_2_h"
+  top: "conv7_2_h"
+}
+layer {
+  name: "conv8_1_h"
+  type: "Convolution"
+  bottom: "conv7_2_h"
+  top: "conv8_1_h"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "conv8_1_relu"
+  type: "ReLU"
+  bottom: "conv8_1_h"
+  top: "conv8_1_h"
+}
+layer {
+  name: "conv8_2_h"
+  type: "Convolution"
+  bottom: "conv8_1_h"
+  top: "conv8_2_h"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 0
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "conv8_2_relu"
+  type: "ReLU"
+  bottom: "conv8_2_h"
+  top: "conv8_2_h"
+}
+layer {
+  name: "conv9_1_h"
+  type: "Convolution"
+  bottom: "conv8_2_h"
+  top: "conv9_1_h"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    pad: 0
+    kernel_size: 1
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "conv9_1_relu"
+  type: "ReLU"
+  bottom: "conv9_1_h"
+  top: "conv9_1_h"
+}
+layer {
+  name: "conv9_2_h"
+  type: "Convolution"
+  bottom: "conv9_1_h"
+  top: "conv9_2_h"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 0
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "conv9_2_relu"
+  type: "ReLU"
+  bottom: "conv9_2_h"
+  top: "conv9_2_h"
+}
+layer {
+  name: "conv4_3_norm"
+  type: "Normalize"
+  bottom: "layer_256_1_bn1"
+  top: "conv4_3_norm"
+  norm_param {
+    across_spatial: false
+    scale_filler {
+      type: "constant"
+      value: 20
+    }
+    channel_shared: false
+  }
+}
+layer {
+  name: "conv4_3_norm_mbox_loc"
+  type: "Convolution"
+  bottom: "conv4_3_norm"
+  top: "conv4_3_norm_mbox_loc"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 16
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "conv4_3_norm_mbox_loc_perm"
+  type: "Permute"
+  bottom: "conv4_3_norm_mbox_loc"
+  top: "conv4_3_norm_mbox_loc_perm"
+  permute_param {
+    order: 0
+    order: 2
+    order: 3
+    order: 1
+  }
+}
+layer {
+  name: "conv4_3_norm_mbox_loc_flat"
+  type: "Flatten"
+  bottom: "conv4_3_norm_mbox_loc_perm"
+  top: "conv4_3_norm_mbox_loc_flat"
+  flatten_param {
+    axis: 1
+  }
+}
+layer {
+  name: "conv4_3_norm_mbox_conf"
+  type: "Convolution"
+  bottom: "conv4_3_norm"
+  top: "conv4_3_norm_mbox_conf"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 8 # 84
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "conv4_3_norm_mbox_conf_perm"
+  type: "Permute"
+  bottom: "conv4_3_norm_mbox_conf"
+  top: "conv4_3_norm_mbox_conf_perm"
+  permute_param {
+    order: 0
+    order: 2
+    order: 3
+    order: 1
+  }
+}
+layer {
+  name: "conv4_3_norm_mbox_conf_flat"
+  type: "Flatten"
+  bottom: "conv4_3_norm_mbox_conf_perm"
+  top: "conv4_3_norm_mbox_conf_flat"
+  flatten_param {
+    axis: 1
+  }
+}
+layer {
+  name: "conv4_3_norm_mbox_priorbox"
+  type: "PriorBox"
+  bottom: "conv4_3_norm"
+  bottom: "data"
+  top: "conv4_3_norm_mbox_priorbox"
+  prior_box_param {
+    min_size: 30.0
+    max_size: 60.0
+    aspect_ratio: 2
+    flip: true
+    clip: false
+    variance: 0.1
+    variance: 0.1
+    variance: 0.2
+    variance: 0.2
+    step: 8
+    offset: 0.5
+  }
+}
+layer {
+  name: "fc7_mbox_loc"
+  type: "Convolution"
+  bottom: "fc7"
+  top: "fc7_mbox_loc"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 24
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "fc7_mbox_loc_perm"
+  type: "Permute"
+  bottom: "fc7_mbox_loc"
+  top: "fc7_mbox_loc_perm"
+  permute_param {
+    order: 0
+    order: 2
+    order: 3
+    order: 1
+  }
+}
+layer {
+  name: "fc7_mbox_loc_flat"
+  type: "Flatten"
+  bottom: "fc7_mbox_loc_perm"
+  top: "fc7_mbox_loc_flat"
+  flatten_param {
+    axis: 1
+  }
+}
+layer {
+  name: "fc7_mbox_conf"
+  type: "Convolution"
+  bottom: "fc7"
+  top: "fc7_mbox_conf"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 12 # 126
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "fc7_mbox_conf_perm"
+  type: "Permute"
+  bottom: "fc7_mbox_conf"
+  top: "fc7_mbox_conf_perm"
+  permute_param {
+    order: 0
+    order: 2
+    order: 3
+    order: 1
+  }
+}
+layer {
+  name: "fc7_mbox_conf_flat"
+  type: "Flatten"
+  bottom: "fc7_mbox_conf_perm"
+  top: "fc7_mbox_conf_flat"
+  flatten_param {
+    axis: 1
+  }
+}
+layer {
+  name: "fc7_mbox_priorbox"
+  type: "PriorBox"
+  bottom: "fc7"
+  bottom: "data"
+  top: "fc7_mbox_priorbox"
+  prior_box_param {
+    min_size: 60.0
+    max_size: 111.0
+    aspect_ratio: 2
+    aspect_ratio: 3
+    flip: true
+    clip: false
+    variance: 0.1
+    variance: 0.1
+    variance: 0.2
+    variance: 0.2
+    step: 16
+    offset: 0.5
+  }
+}
+layer {
+  name: "conv6_2_mbox_loc"
+  type: "Convolution"
+  bottom: "conv6_2_h"
+  top: "conv6_2_mbox_loc"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 24
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "conv6_2_mbox_loc_perm"
+  type: "Permute"
+  bottom: "conv6_2_mbox_loc"
+  top: "conv6_2_mbox_loc_perm"
+  permute_param {
+    order: 0
+    order: 2
+    order: 3
+    order: 1
+  }
+}
+layer {
+  name: "conv6_2_mbox_loc_flat"
+  type: "Flatten"
+  bottom: "conv6_2_mbox_loc_perm"
+  top: "conv6_2_mbox_loc_flat"
+  flatten_param {
+    axis: 1
+  }
+}
+layer {
+  name: "conv6_2_mbox_conf"
+  type: "Convolution"
+  bottom: "conv6_2_h"
+  top: "conv6_2_mbox_conf"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 12 # 126
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "conv6_2_mbox_conf_perm"
+  type: "Permute"
+  bottom: "conv6_2_mbox_conf"
+  top: "conv6_2_mbox_conf_perm"
+  permute_param {
+    order: 0
+    order: 2
+    order: 3
+    order: 1
+  }
+}
+layer {
+  name: "conv6_2_mbox_conf_flat"
+  type: "Flatten"
+  bottom: "conv6_2_mbox_conf_perm"
+  top: "conv6_2_mbox_conf_flat"
+  flatten_param {
+    axis: 1
+  }
+}
+layer {
+  name: "conv6_2_mbox_priorbox"
+  type: "PriorBox"
+  bottom: "conv6_2_h"
+  bottom: "data"
+  top: "conv6_2_mbox_priorbox"
+  prior_box_param {
+    min_size: 111.0
+    max_size: 162.0
+    aspect_ratio: 2
+    aspect_ratio: 3
+    flip: true
+    clip: false
+    variance: 0.1
+    variance: 0.1
+    variance: 0.2
+    variance: 0.2
+    step: 32
+    offset: 0.5
+  }
+}
+layer {
+  name: "conv7_2_mbox_loc"
+  type: "Convolution"
+  bottom: "conv7_2_h"
+  top: "conv7_2_mbox_loc"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 24
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "conv7_2_mbox_loc_perm"
+  type: "Permute"
+  bottom: "conv7_2_mbox_loc"
+  top: "conv7_2_mbox_loc_perm"
+  permute_param {
+    order: 0
+    order: 2
+    order: 3
+    order: 1
+  }
+}
+layer {
+  name: "conv7_2_mbox_loc_flat"
+  type: "Flatten"
+  bottom: "conv7_2_mbox_loc_perm"
+  top: "conv7_2_mbox_loc_flat"
+  flatten_param {
+    axis: 1
+  }
+}
+layer {
+  name: "conv7_2_mbox_conf"
+  type: "Convolution"
+  bottom: "conv7_2_h"
+  top: "conv7_2_mbox_conf"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 12 # 126
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "conv7_2_mbox_conf_perm"
+  type: "Permute"
+  bottom: "conv7_2_mbox_conf"
+  top: "conv7_2_mbox_conf_perm"
+  permute_param {
+    order: 0
+    order: 2
+    order: 3
+    order: 1
+  }
+}
+layer {
+  name: "conv7_2_mbox_conf_flat"
+  type: "Flatten"
+  bottom: "conv7_2_mbox_conf_perm"
+  top: "conv7_2_mbox_conf_flat"
+  flatten_param {
+    axis: 1
+  }
+}
+layer {
+  name: "conv7_2_mbox_priorbox"
+  type: "PriorBox"
+  bottom: "conv7_2_h"
+  bottom: "data"
+  top: "conv7_2_mbox_priorbox"
+  prior_box_param {
+    min_size: 162.0
+    max_size: 213.0
+    aspect_ratio: 2
+    aspect_ratio: 3
+    flip: true
+    clip: false
+    variance: 0.1
+    variance: 0.1
+    variance: 0.2
+    variance: 0.2
+    step: 64
+    offset: 0.5
+  }
+}
+layer {
+  name: "conv8_2_mbox_loc"
+  type: "Convolution"
+  bottom: "conv8_2_h"
+  top: "conv8_2_mbox_loc"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 16
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "conv8_2_mbox_loc_perm"
+  type: "Permute"
+  bottom: "conv8_2_mbox_loc"
+  top: "conv8_2_mbox_loc_perm"
+  permute_param {
+    order: 0
+    order: 2
+    order: 3
+    order: 1
+  }
+}
+layer {
+  name: "conv8_2_mbox_loc_flat"
+  type: "Flatten"
+  bottom: "conv8_2_mbox_loc_perm"
+  top: "conv8_2_mbox_loc_flat"
+  flatten_param {
+    axis: 1
+  }
+}
+layer {
+  name: "conv8_2_mbox_conf"
+  type: "Convolution"
+  bottom: "conv8_2_h"
+  top: "conv8_2_mbox_conf"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 8 # 84
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "conv8_2_mbox_conf_perm"
+  type: "Permute"
+  bottom: "conv8_2_mbox_conf"
+  top: "conv8_2_mbox_conf_perm"
+  permute_param {
+    order: 0
+    order: 2
+    order: 3
+    order: 1
+  }
+}
+layer {
+  name: "conv8_2_mbox_conf_flat"
+  type: "Flatten"
+  bottom: "conv8_2_mbox_conf_perm"
+  top: "conv8_2_mbox_conf_flat"
+  flatten_param {
+    axis: 1
+  }
+}
+layer {
+  name: "conv8_2_mbox_priorbox"
+  type: "PriorBox"
+  bottom: "conv8_2_h"
+  bottom: "data"
+  top: "conv8_2_mbox_priorbox"
+  prior_box_param {
+    min_size: 213.0
+    max_size: 264.0
+    aspect_ratio: 2
+    flip: true
+    clip: false
+    variance: 0.1
+    variance: 0.1
+    variance: 0.2
+    variance: 0.2
+    step: 100
+    offset: 0.5
+  }
+}
+layer {
+  name: "conv9_2_mbox_loc"
+  type: "Convolution"
+  bottom: "conv9_2_h"
+  top: "conv9_2_mbox_loc"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 16
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "conv9_2_mbox_loc_perm"
+  type: "Permute"
+  bottom: "conv9_2_mbox_loc"
+  top: "conv9_2_mbox_loc_perm"
+  permute_param {
+    order: 0
+    order: 2
+    order: 3
+    order: 1
+  }
+}
+layer {
+  name: "conv9_2_mbox_loc_flat"
+  type: "Flatten"
+  bottom: "conv9_2_mbox_loc_perm"
+  top: "conv9_2_mbox_loc_flat"
+  flatten_param {
+    axis: 1
+  }
+}
+layer {
+  name: "conv9_2_mbox_conf"
+  type: "Convolution"
+  bottom: "conv9_2_h"
+  top: "conv9_2_mbox_conf"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 8 # 84
+    pad: 1
+    kernel_size: 3
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "conv9_2_mbox_conf_perm"
+  type: "Permute"
+  bottom: "conv9_2_mbox_conf"
+  top: "conv9_2_mbox_conf_perm"
+  permute_param {
+    order: 0
+    order: 2
+    order: 3
+    order: 1
+  }
+}
+layer {
+  name: "conv9_2_mbox_conf_flat"
+  type: "Flatten"
+  bottom: "conv9_2_mbox_conf_perm"
+  top: "conv9_2_mbox_conf_flat"
+  flatten_param {
+    axis: 1
+  }
+}
+layer {
+  name: "conv9_2_mbox_priorbox"
+  type: "PriorBox"
+  bottom: "conv9_2_h"
+  bottom: "data"
+  top: "conv9_2_mbox_priorbox"
+  prior_box_param {
+    min_size: 264.0
+    max_size: 315.0
+    aspect_ratio: 2
+    flip: true
+    clip: false
+    variance: 0.1
+    variance: 0.1
+    variance: 0.2
+    variance: 0.2
+    step: 300
+    offset: 0.5
+  }
+}
+layer {
+  name: "mbox_loc"
+  type: "Concat"
+  bottom: "conv4_3_norm_mbox_loc_flat"
+  bottom: "fc7_mbox_loc_flat"
+  bottom: "conv6_2_mbox_loc_flat"
+  bottom: "conv7_2_mbox_loc_flat"
+  bottom: "conv8_2_mbox_loc_flat"
+  bottom: "conv9_2_mbox_loc_flat"
+  top: "mbox_loc"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "mbox_conf"
+  type: "Concat"
+  bottom: "conv4_3_norm_mbox_conf_flat"
+  bottom: "fc7_mbox_conf_flat"
+  bottom: "conv6_2_mbox_conf_flat"
+  bottom: "conv7_2_mbox_conf_flat"
+  bottom: "conv8_2_mbox_conf_flat"
+  bottom: "conv9_2_mbox_conf_flat"
+  top: "mbox_conf"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "mbox_priorbox"
+  type: "Concat"
+  bottom: "conv4_3_norm_mbox_priorbox"
+  bottom: "fc7_mbox_priorbox"
+  bottom: "conv6_2_mbox_priorbox"
+  bottom: "conv7_2_mbox_priorbox"
+  bottom: "conv8_2_mbox_priorbox"
+  bottom: "conv9_2_mbox_priorbox"
+  top: "mbox_priorbox"
+  concat_param {
+    axis: 2
+  }
+}
+
+layer {
+  name: "mbox_conf_reshape"
+  type: "Reshape"
+  bottom: "mbox_conf"
+  top: "mbox_conf_reshape"
+  reshape_param {
+    shape {
+      dim: 0
+      dim: -1
+      dim: 2
+    }
+  }
+}
+layer {
+  name: "mbox_conf_softmax"
+  type: "Softmax"
+  bottom: "mbox_conf_reshape"
+  top: "mbox_conf_softmax"
+  softmax_param {
+    axis: 2
+  }
+}
+layer {
+  name: "mbox_conf_flatten"
+  type: "Flatten"
+  bottom: "mbox_conf_softmax"
+  top: "mbox_conf_flatten"
+  flatten_param {
+    axis: 1
+  }
+}
+
+layer {
+  name: "detection_out"
+  type: "DetectionOutput"
+  bottom: "mbox_loc"
+  bottom: "mbox_conf_flatten"
+  bottom: "mbox_priorbox"
+  top: "detection_out"
+  include {
+    phase: TEST
+  }
+  detection_output_param {
+    num_classes: 2
+    share_location: true
+    background_label_id: 0
+    nms_param {
+      nms_threshold: 0.45
+      top_k: 400
+    }
+    code_type: CENTER_SIZE
+    keep_top_k: 200
+    confidence_threshold: 0.01
+    clip: 1
+  }
+}
diff --git a/subprojects/shotwell-facedetect/facedetect-opencv.cpp b/subprojects/shotwell-facedetect/facedetect-opencv.cpp
new file mode 100644
index 0000000..8b0ad10
--- /dev/null
+++ b/subprojects/shotwell-facedetect/facedetect-opencv.cpp
@@ -0,0 +1,281 @@
+// SPDX-License-Identifier: LGPL-2.1-or-later
+
+#include "shotwell-facedetect.hpp"
+
+#include <opencv2/imgcodecs.hpp>
+#include <opencv2/imgproc/imgproc.hpp>
+#include <opencv2/objdetect/objdetect.hpp>
+
+#ifdef HAS_OPENCV_DNN
+    #include <opencv2/dnn.hpp>
+#endif
+
+#include <iostream>
+#include <string>
+#include <filesystem>
+
+// Global variable for DNN to generate vector out of face
+#ifdef HAS_OPENCV_DNN
+static cv::dnn::Net faceRecogNet;
+static cv::dnn::Net faceDetectNet;
+#endif
+
+static cv::CascadeClassifier cascade;
+static cv::CascadeClassifier cascade_profile;
+static bool disableDnn{ true };
+
+constexpr std::string_view PROTOTEXT_FILE{ "deploy.prototxt" };
+constexpr std::string_view OPENFACE_RECOG_TORCH_NET{ "openface.nn4.small2.v1.t7" };
+constexpr std::string_view RESNET_DETECT_CAFFE_NET{ "res10_300x300_ssd_iter_140000_fp16.caffemodel" };
+constexpr std::string_view HAARCASCADE{ "haarcascade_frontalface_alt.xml" };
+constexpr std::string_view HAARCASCADE_PROFILE{ "haarcascade_profileface.xml" };
+
+std::vector<cv::Rect> detectFacesMat(const cv::Mat &img);
+std::vector<double> faceToVecMat(const cv::Mat& img);
+
+// Detect faces in a photo
+std::vector<FaceRect> detectFaces(const cv::String &inputName, double scale, bool infer = false) {
+    if(cascade.empty()) {
+        g_warning("No cascade file loaded. Did you call loadNet()?");
+        return {};
+    }
+
+	if (inputName.empty()) {
+        g_warning("No file to process. aborting");
+        return {};
+	}
+
+    cv::Mat const img = cv::imread(inputName, 1);
+	if (img.empty()) {
+        g_warning("Failed to load the image file: %s", inputName.c_str());
+        return {};
+	}
+
+    std::vector<cv::Rect> faces;
+    cv::Size smallImgSize;
+
+#ifdef HAS_OPENCV_DNN
+    disableDnn = faceDetectNet.empty();
+#else
+    disableDnn = true;
+#endif
+    try {
+        if (disableDnn) {
+            // Classical face detection
+            cv::Mat gray;
+            cvtColor(img, gray, cv::COLOR_BGR2GRAY);
+
+            scale = 1.0;
+            cv::Mat smallImg(cvRound(img.rows / scale), cvRound(img.cols / scale), CV_8UC1);
+            smallImgSize = smallImg.size();
+
+            cv::resize(gray, smallImg, smallImgSize, 0, 0, cv::INTER_LINEAR);
+            cv::equalizeHist(smallImg, smallImg);
+            constexpr double SCALE_FACTOR_FRONTAL{ 1.1 };
+            constexpr double SCALE_FACTOR_PROFILE{ 1.05 };
+            constexpr int MIN_NEIGHBOURS{ 2 };
+            constexpr int MIN_SIZE{ 30 };
+            cascade.detectMultiScale (smallImg,
+                                    faces,
+                                    SCALE_FACTOR_FRONTAL,
+                                    MIN_NEIGHBOURS,
+                                    cv::CASCADE_SCALE_IMAGE,
+                                    cv::Size (MIN_SIZE, MIN_SIZE));
+
+            // Run the cascade for profile faces, if available
+            if(not cascade_profile.empty()) {
+                g_debug("Running haarcascade detection for profile faces");
+                std::vector<cv::Rect> profiles;
+                cascade_profile.detectMultiScale (smallImg,
+                                                profiles,
+                                                SCALE_FACTOR_PROFILE,
+                                                MIN_NEIGHBOURS,
+                                                cv::CASCADE_SCALE_IMAGE,
+                                                cv::Size (MIN_SIZE, MIN_SIZE));
+                if(not profiles.empty()) {
+                    faces.insert(faces.end(), profiles.begin(), profiles.end());
+                }
+
+                // Duplicate all rectangles so we can safely run groupRectangles with minimum 1 on it - otherwise
+                // OpenCV does weird things
+                faces.insert(faces.end(), faces.begin(), faces.end());
+
+                // Try to merge all overlapping rectangles
+                cv::groupRectangles(faces, 1);
+            }
+        } else {
+    #ifdef HAS_OPENCV_DNN
+            // DNN based face detection
+            faces = detectFacesMat(img);
+            smallImgSize = img.size(); // Not using the small image here
+    #endif
+        }
+    } catch (cv::Exception& ex) {
+        g_warning("Face detection failed: %s", ex.what());
+        return {};
+    }
+
+    std::vector<FaceRect> scaled;
+    for (std::vector<cv::Rect>::const_iterator r = faces.begin(); r != faces.end(); r++) {
+        FaceRect i;
+        i.x = (float) r->x / smallImgSize.width;
+        i.y = (float) r->y / smallImgSize.height;
+        i.width = (float) r->width / smallImgSize.width;
+        i.height = (float) r->height / smallImgSize.height;
+
+#ifdef HAS_OPENCV_DNN
+        try {
+            if (infer && !faceRecogNet.empty()) {
+                // Get colour image for vector generation
+                cv::Mat colourImg;
+                cv::resize(img, colourImg, smallImgSize, 0, 0, cv::INTER_LINEAR);
+                i.vec = faceToVecMat(colourImg(*r)); // Run vector conversion on the face
+            }
+        } catch (cv::Exception& ex) {
+            g_warning("Face recognition failed: %s", ex.what());
+            i.vec = {};
+        }
+#endif
+        scaled.push_back(i);
+    }
+
+    return scaled;
+}
+
+// Load network into global var
+bool loadNet(const cv::String &baseDir)
+{
+    // Split baseDir into multiple search paths
+    std::stringstream iss{ baseDir };
+    std::string path;
+    while(std::getline(iss, path, ':')) {
+        g_debug("Looking for face detection data files in %s", path.c_str());
+
+        std::filesystem::path const base_path{ path };
+
+        auto haarcascade = base_path / HAARCASCADE;
+        if(cascade.empty()) {
+            cascade.load(haarcascade);
+        }
+
+        if(cascade.empty()) {
+            g_info("%s not found", haarcascade.c_str());
+        }
+
+        auto haarcascade_profile = base_path / HAARCASCADE_PROFILE;
+        if(cascade_profile.empty()) {
+            cascade_profile.load(haarcascade_profile);
+        }
+
+        if(cascade_profile.empty()) {
+            g_info("%s not found", haarcascade_profile.c_str());
+        }
+
+#if HAS_OPENCV_DNN
+
+        if(faceDetectNet.empty()) {
+            try {
+                faceDetectNet =
+                    cv::dnn::readNetFromCaffe(base_path / PROTOTEXT_FILE, base_path / RESNET_DETECT_CAFFE_NET);
+            } catch(cv::Exception &e) {
+                g_info("Failed to load face detect net: %s", e.what());
+            }
+        }
+
+        if(faceRecogNet.empty()) {
+            try {
+                faceRecogNet = cv::dnn::readNetFromTorch(base_path / OPENFACE_RECOG_TORCH_NET);
+            } catch(cv::Exception &e) {
+                g_info("Failed to load face recognition net: %s", e.what());
+            }
+        }
+#endif
+    }
+
+    if(cascade.empty() && cascade_profile.empty() && faceDetectNet.empty()) {
+        g_warning("No face detection method detected. Face detection fill not work.");
+        return false;
+    }
+
+#if HAS_OPENCV_DNN
+    // If there is no detection model, disable advanced face detection
+    disableDnn = faceDetectNet.empty();
+
+    if(faceRecogNet.empty()) {
+        g_warning("Face recognition net not available, disabling recognition");
+    }
+
+    return true;
+#else
+    return not cascade.empty() && not cascade_profile.empty();
+#endif
+}
+
+// Face detector
+// Adapted from OpenCV example:
+// https://github.com/opencv/opencv/blob/master/samples/dnn/js_face_recognition.html
+std::vector<cv::Rect> detectFacesMat(const cv::Mat& img) {
+    std::vector<cv::Rect> faces;
+#ifdef HAS_OPENCV_DNN
+    const cv::Mat blob = cv::dnn::blobFromImage(img, 1.0, cv::Size(128*8, 96*8),
+                                          cv::Scalar(104, 177, 123, 0), false, false);
+    faceDetectNet.setInput(blob);
+    cv::Mat out = faceDetectNet.forward();
+    // out is a 4D matrix [1 x 1 x n x 7]
+    // n - number of results
+    assert(out.dims == 4);
+    int outIdx[4] = { 0, 0, 0, 0 };
+    auto result_size = out.size[2];
+    for (auto i = 0; i < result_size; i++) {
+        outIdx[2] = i; outIdx[3] = 2;
+        const auto confidence = out.at<float>(outIdx);
+        outIdx[3]++;
+        auto left = out.at<float>(outIdx) * (double)img.cols;
+        outIdx[3]++;
+        auto top = out.at<float>(outIdx) * (double)img.rows;
+        outIdx[3]++;
+        auto right = out.at<float>(outIdx) * (double)img.cols;
+        outIdx[3]++;
+        auto bottom = out.at<float> (outIdx) * (double)img.rows;
+        left = std::clamp (left, 0.0, (double) img.cols - 1);
+        right = std::clamp (right, 0.0, (double) img.cols - 1);
+        bottom = std::clamp (bottom, 0.0, (double) img.rows - 1);
+        top = std::clamp (top, 0.0, (double) img.rows - 1);
+
+        constexpr double CONFIDENCE_THRESHOLD{ 0.98 };
+        if (confidence > CONFIDENCE_THRESHOLD && left < right && top < bottom) {
+            const cv::Rect rect (static_cast<int> (left),
+                                 static_cast<int> (top),
+                                 static_cast<int> (right - left),
+                                 static_cast<int> (bottom - top));
+            faces.push_back(rect);
+        }
+    }
+#endif // HAS_OPENCV_DNN
+    return faces;
+}
+
+// Face to vector converter
+// Adapted from OpenCV example:
+// https://github.com/opencv/opencv/blob/master/samples/dnn/js_face_recognition.html
+#ifdef HAS_OPENCV_DNN
+std::vector<double> faceToVecMat(const cv::Mat &img) {
+    std::vector<double> ret;
+    constexpr int SMALL_IMAGE_SIZE{ 96 };
+    cv::Mat smallImg(SMALL_IMAGE_SIZE, SMALL_IMAGE_SIZE, CV_8UC1);
+    const cv::Size smallImgSize = smallImg.size();
+
+    cv::resize(img, smallImg, smallImgSize, 0, 0, cv::INTER_LINEAR);
+    // Generate 128 element face vector using DNN
+    constexpr double SCALE_FACTOR{ 1.0 / 255.0 };
+    const cv::Mat blob = cv::dnn::blobFromImage (smallImg, SCALE_FACTOR, smallImgSize, cv::Scalar (), true, false);
+
+    faceRecogNet.setInput(blob);
+    cv::Mat vec = faceRecogNet.forward();
+    // Return vector
+    for (int i = 0; i < vec.rows; ++i) {
+        ret.insert(ret.end(), vec.ptr<float>(i), vec.ptr<float>(i) + vec.cols);
+    }
+    return ret;
+}
+#endif
diff --git a/subprojects/shotwell-facedetect/haarcascade_frontalface_alt.xml b/subprojects/shotwell-facedetect/haarcascade_frontalface_alt.xml
new file mode 100644
index 0000000..ade4b21
--- /dev/null
+++ b/subprojects/shotwell-facedetect/haarcascade_frontalface_alt.xml
@@ -0,0 +1,24350 @@
+<?xml version="1.0"?>
+<!--
+    Stump-based 20x20 gentle adaboost frontal face detector.
+    Created by Rainer Lienhart.
+
+////////////////////////////////////////////////////////////////////////////////////////
+
+  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+
+  By downloading, copying, installing or using the software you agree to this license.
+  If you do not agree to this license, do not download, install,
+  copy or use the software.
+
+
+                        Intel License Agreement
+                For Open Source Computer Vision Library
+
+ Copyright (C) 2000, Intel Corporation, all rights reserved.
+ Third party copyrights are property of their respective owners.
+
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+   * Redistribution's of source code must retain the above copyright notice,
+     this list of conditions and the following disclaimer.
+
+   * Redistribution's in binary form must reproduce the above copyright notice,
+     this list of conditions and the following disclaimer in the documentation
+     and/or other materials provided with the distribution.
+
+   * The name of Intel Corporation may not be used to endorse or promote products
+     derived from this software without specific prior written permission.
+
+ This software is provided by the copyright holders and contributors "as is" and
+ any express or implied warranties, including, but not limited to, the implied
+ warranties of merchantability and fitness for a particular purpose are disclaimed.
+ In no event shall the Intel Corporation or contributors be liable for any direct,
+ indirect, incidental, special, exemplary, or consequential damages
+ (including, but not limited to, procurement of substitute goods or services;
+ loss of use, data, or profits; or business interruption) however caused
+ and on any theory of liability, whether in contract, strict liability,
+ or tort (including negligence or otherwise) arising in any way out of
+ the use of this software, even if advised of the possibility of such damage.
+-->
+<opencv_storage>
+<cascade type_id="opencv-cascade-classifier"><stageType>BOOST</stageType>
+  <featureType>HAAR</featureType>
+  <height>20</height>
+  <width>20</width>
+  <stageParams>
+    <maxWeakCount>213</maxWeakCount></stageParams>
+  <featureParams>
+    <maxCatCount>0</maxCatCount></featureParams>
+  <stageNum>22</stageNum>
+  <stages>
+    <_>
+      <maxWeakCount>3</maxWeakCount>
+      <stageThreshold>8.2268941402435303e-01</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 0 4.0141958743333817e-03</internalNodes>
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+          <leafValues>
+            9.0049281716346741e-02 6.3748198747634888e-01</leafValues></_></weakClassifiers></_>
+    <_>
+      <maxWeakCount>16</maxWeakCount>
+      <stageThreshold>6.9566087722778320e+00</stageThreshold>
+      <weakClassifiers>
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diff --git a/subprojects/shotwell-facedetect/haarcascade_profileface.xml b/subprojects/shotwell-facedetect/haarcascade_profileface.xml
new file mode 100644
index 0000000..486d8e3
--- /dev/null
+++ b/subprojects/shotwell-facedetect/haarcascade_profileface.xml
@@ -0,0 +1,29690 @@
+<?xml version="1.0"?>
+<!--
+    20x20 profile face detector.
+    Contributed by David Bradley from Princeton University.
+
+////////////////////////////////////////////////////////////////////////////////////////
+
+  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+
+  By downloading, copying, installing or using the software you agree to this license.
+  If you do not agree to this license, do not download, install,
+  copy or use the software.
+
+
+                        Intel License Agreement
+                For Open Source Computer Vision Library
+
+ Copyright (C) 2000, Intel Corporation, all rights reserved.
+ Third party copyrights are property of their respective owners.
+
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+   * Redistribution's of source code must retain the above copyright notice,
+     this list of conditions and the following disclaimer.
+
+   * Redistribution's in binary form must reproduce the above copyright notice,
+     this list of conditions and the following disclaimer in the documentation
+     and/or other materials provided with the distribution.
+
+   * The name of Intel Corporation may not be used to endorse or promote products
+     derived from this software without specific prior written permission.
+
+ This software is provided by the copyright holders and contributors "as is" and
+ any express or implied warranties, including, but not limited to, the implied
+ warranties of merchantability and fitness for a particular purpose are disclaimed.
+ In no event shall the Intel Corporation or contributors be liable for any direct,
+ indirect, incidental, special, exemplary, or consequential damages
+ (including, but not limited to, procurement of substitute goods or services;
+ loss of use, data, or profits; or business interruption) however caused
+ and on any theory of liability, whether in contract, strict liability,
+ or tort (including negligence or otherwise) arising in any way out of
+ the use of this software, even if advised of the possibility of such damage.
+-->
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+  <featureType>HAAR</featureType>
+  <height>20</height>
+  <width>20</width>
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+</opencv_storage>
diff --git a/subprojects/shotwell-facedetect/meson.build b/subprojects/shotwell-facedetect/meson.build
new file mode 100644
index 0000000..70570e4
--- /dev/null
+++ b/subprojects/shotwell-facedetect/meson.build
@@ -0,0 +1,62 @@
+project('shotwell-facedetect', ['c', 'cpp'], default_options : ['cpp_std=c++17'])
+gnome = import('gnome')
+facedetect_dep = dependency('opencv4', version : ['>= 4.0.0'], required : false)
+if not facedetect_dep.found()
+    facedetect_dep = dependency('opencv', version : ['>= 3.4.0'], required : true)
+endif
+cpp = meson.get_compiler('cpp')
+has_dnn = cpp.has_header('opencv2/dnn.hpp', dependencies: facedetect_dep)
+if has_dnn
+  dnn_define = declare_dependency(compile_args: '-DHAS_OPENCV_DNN')
+else
+  dnn_define = []
+endif
+
+libexecdir = join_paths(get_option('libexecdir'), 'shotwell')
+
+gio = dependency('gio-2.0', version: '>= 2.40')
+gio_unix = dependency('gio-unix-2.0', required : true)
+gdbus_src = gnome.gdbus_codegen('dbus-interface',
+  sources: 'org.gnome.ShotwellFaces1.xml',
+  interface_prefix : 'org.gnome.')
+
+con = configuration_data()
+con.set('libexecdir', join_paths(get_option('prefix'), libexecdir))
+
+if meson.is_subproject()
+    config_incdir = include_directories('../..')
+else
+    config_incdir = include_directories('.')
+    configure_file(
+        input: 'org.gnome.Shotwell.Faces1.desktop.in',
+        output: 'org.gnome.Shotwell.Faces1.desktop',
+        configuration: con,
+        install: true,
+        install_dir : join_paths(get_option('datadir'), 'applications')
+    )
+endif
+
+executable('shotwell-facedetect',
+           'shotwell-facedetect.cpp', 'facedetect-opencv.cpp', gdbus_src,
+           dependencies : [facedetect_dep, gio, gio_unix, dnn_define],
+           install : true,
+           include_directories: config_incdir,
+           install_dir : libexecdir)
+install_data('haarcascade_frontalface_alt.xml',
+              install_dir : join_paths(get_option('datadir'), 'shotwell', 'facedetect'))
+install_data('haarcascade_profileface.xml',
+              install_dir : join_paths(get_option('datadir'), 'shotwell', 'facedetect'))
+install_data(
+  'openface.nn4.small2.v1.t7',
+  install_dir: join_paths(get_option('datadir'), 'shotwell', 'facedetect')
+)
+
+install_data('deploy.prototxt', install_dir: join_paths(get_option('datadir'), 'shotwell', 'facedetect'))
+
+configure_file(
+    input : 'org.gnome.Shotwell.Faces1.service.in',
+    output : 'org.gnome.Shotwell.Faces1.service',
+    configuration: con,
+    install: true,
+    install_dir : join_paths(get_option('datadir'), 'dbus-1', 'services')
+    )
diff --git a/subprojects/shotwell-facedetect/openface.nn4.small2.v1.t7 b/subprojects/shotwell-facedetect/openface.nn4.small2.v1.t7
new file mode 100644
index 0000000..8a0d019
--- /dev/null
+++ b/subprojects/shotwell-facedetect/openface.nn4.small2.v1.t7
diff --git a/subprojects/shotwell-facedetect/org.gnome.Shotwell.Faces1.desktop.in b/subprojects/shotwell-facedetect/org.gnome.Shotwell.Faces1.desktop.in
new file mode 100644
index 0000000..d64714e
--- /dev/null
+++ b/subprojects/shotwell-facedetect/org.gnome.Shotwell.Faces1.desktop.in
@@ -0,0 +1,7 @@
+[Desktop Entry]
+Version=1.0
+Name=Shotwell Face detection
+Comment=Shotwell face detection and recognition backend
+NoDisplay=true
+Type=Application
+Exec=@libexecdir@/shotwell-facedetect
diff --git a/subprojects/shotwell-facedetect/org.gnome.Shotwell.Faces1.service.in b/subprojects/shotwell-facedetect/org.gnome.Shotwell.Faces1.service.in
new file mode 100644
index 0000000..127d744
--- /dev/null
+++ b/subprojects/shotwell-facedetect/org.gnome.Shotwell.Faces1.service.in
@@ -0,0 +1,3 @@
+[D-BUS Service]
+Name=org.gnome.Shotwell.Faces1
+Exec=@libexecdir@/shotwell-facedetect
\ No newline at end of file
diff --git a/subprojects/shotwell-facedetect/org.gnome.ShotwellFaces1.xml b/subprojects/shotwell-facedetect/org.gnome.ShotwellFaces1.xml
new file mode 100644
index 0000000..3bf1071
--- /dev/null
+++ b/subprojects/shotwell-facedetect/org.gnome.ShotwellFaces1.xml
@@ -0,0 +1,42 @@
+<!DOCTYPE node PUBLIC
+'-//freedesktop//DTD D-BUS Object Introspection 1.0//EN'
+'http://www.freedesktop.org/standards/dbus/1.0/introspect.dtd'>
+<node>
+  <!--
+      org.gnome.Shotwell.Faces1:
+      @short_description: Face detection/recognition
+  -->
+  <interface name="org.gnome.Shotwell.Faces1">
+    <!--
+        DetectFaces
+        @image: Image file to run face detection on
+        @cascade: Cascade XML file - unused
+        @scale: Scaling to apply on image
+        @infer: Provide an
+        Returns an array of face bounding boxes (x,y,w,h) in dimensionless units
+    -->
+    <method name="DetectFaces">
+      <arg type="s" name="image" direction="in" />
+      <arg type="s" name="cascade" direction="in" />
+      <arg type="d" name="scale" direction="in" />
+      <arg type="b" name="infer" direction="in" />
+      <arg type="a(ddddad)" name="faces" direction="out" />
+    </method>
+
+    <!--
+        LoadNet
+        @net: path to folder containing the DNN
+        Returns non-zero on any error
+    -->
+    <method name="LoadNet">
+      <arg type="s" name="net" direction="in" />
+      <arg type="b" name="ret" direction="out" />
+    </method>
+
+    <!--
+        Terminate
+    -->
+    <method name="Terminate">
+    </method>
+  </interface>
+</node>
diff --git a/subprojects/shotwell-facedetect/shotwell-facedetect.cpp b/subprojects/shotwell-facedetect/shotwell-facedetect.cpp
new file mode 100644
index 0000000..3f8edeb
--- /dev/null
+++ b/subprojects/shotwell-facedetect/shotwell-facedetect.cpp
@@ -0,0 +1,167 @@
+/* Copyright 2016 Software Freedom Conservancy Inc.
+ *
+ * Copyright 2011 Valentín Barros Puertas <valentin(at)sanva(dot)net>
+ * Copyright 2018 Ricardo Fantin da Costa <ricardofantin(at)gmail(dot)com>
+ * Copyright 2018 Narendra A <narendra_m_a(at)yahoo(dot)com>
+ * 
+ * This software is licensed under the GNU LGPL (version 2.1 or later).
+ * See the COPYING file in this distribution.
+ */
+
+#include "shotwell-facedetect.hpp"
+#include "dbus-interface.h"
+
+#include <gio/gio.h>
+#include <glib.h>
+
+#include <iostream>
+
+constexpr std::string_view FACEDETECT_INTERFACE_NAME{ "org.gnome.Shotwell.Faces1" };
+constexpr std::string_view FACEDETECT_PATH{ "/org/gnome/shotwell/faces" };
+
+GVariant *FaceRect::serialize() const
+{
+    return g_variant_new("(dddd@ad)", x, y, width, height,
+                         g_variant_new_fixed_array(G_VARIANT_TYPE_DOUBLE, vec.data(), vec.size(), sizeof(double)));
+}
+
+// DBus binding functions
+static gboolean on_handle_detect_faces(ShotwellFaces1 *object, GDBusMethodInvocation *invocation,
+                                       [[maybe_unused]]const gchar *arg_image, const gchar *arg_cascade, gdouble arg_scale,
+                                       gboolean arg_infer)
+{
+    g_auto(GVariantBuilder) builder = G_VARIANT_BUILDER_INIT(G_VARIANT_TYPE("a(ddddad)"));
+    auto rects = detectFaces(arg_image, arg_scale, arg_infer == TRUE);
+
+    // Construct return value
+    for(const auto &rect : rects) {
+        g_variant_builder_add(&builder, "@(ddddad)", rect.serialize());
+        g_debug("Returning %f,%f-%f", rect.x, rect.y, rect.vec.back());
+    }
+
+    // Call return
+    shotwell_faces1_complete_detect_faces(object, invocation, g_variant_builder_end(&builder));
+    return TRUE;
+}
+
+static gboolean on_handle_load_net(ShotwellFaces1 *object, GDBusMethodInvocation *invocation, const gchar *arg_net)
+{
+    // Call return
+    shotwell_faces1_complete_load_net(object, invocation, loadNet(arg_net) ? TRUE : FALSE);
+    return TRUE;
+}
+
+static gboolean on_handle_terminate(ShotwellFaces1 *object, GDBusMethodInvocation *invocation, gpointer user_data)
+{
+    g_debug("Exiting...");
+    shotwell_faces1_complete_terminate(object, invocation);
+    g_main_loop_quit(static_cast<GMainLoop *>(user_data));
+
+    return TRUE;
+}
+
+static void on_name_acquired(GDBusConnection *connection,
+                             const gchar *name, gpointer user_data) {
+    g_debug("Got name %s", name);
+
+    auto *interface = shotwell_faces1_skeleton_new();
+    g_signal_connect(interface, "handle-detect-faces", G_CALLBACK (on_handle_detect_faces), nullptr);
+    g_signal_connect(interface, "handle-terminate", G_CALLBACK (on_handle_terminate), user_data);
+    g_signal_connect(interface, "handle-load-net", G_CALLBACK (on_handle_load_net), nullptr);
+
+    g_autoptr(GError) error = nullptr;
+    g_dbus_interface_skeleton_export(G_DBUS_INTERFACE_SKELETON(interface), connection, FACEDETECT_PATH.data(), &error);
+    if (error != nullptr) {
+        g_print("Failed to export interface: %s", error->message);
+    }
+}
+
+static void on_name_lost(GDBusConnection *connection,
+                         const gchar *name, gpointer user_data) {
+    if (connection == nullptr) {
+        g_debug("Unable to establish connection for name %s", name);
+    } else {
+        g_debug("Connection for name %s disconnected", name);
+    }
+    g_main_loop_quit(static_cast<GMainLoop *>(user_data));
+}
+
+static char* address = nullptr;
+
+static GOptionEntry entries[] = {
+    { "address", 'a', 0, G_OPTION_ARG_STRING, &address, "Use private DBus ADDRESS instead of session", "ADDRESS" },
+    { nullptr }
+};
+
+static gboolean on_authorize_authenticated_peer([[maybe_unused]] GIOStream *iostream, GCredentials *credentials,
+                                                [[maybe_unused]] gpointer user_data)
+{
+    g_autoptr(GCredentials) own_credentials = nullptr;
+
+    g_debug("Authorizing peer with credentials %s\n", g_credentials_to_string(credentials));
+
+    if(credentials == nullptr) {
+        return FALSE;
+    }
+
+    own_credentials = g_credentials_new();
+
+    {
+        g_autoptr(GError) error = nullptr;
+
+        if(g_credentials_is_same_user(credentials, own_credentials, &error) == FALSE) {
+            g_warning("Unable to authorize peer: %s", error->message);
+
+            return FALSE;
+        }
+    }
+
+    return TRUE;
+}
+
+int main(int argc, char **argv) {
+    GMainLoop *loop;
+    GError *error = nullptr;
+    GOptionContext *context;
+
+    context = g_option_context_new ("- Shotwell face detection helper service");
+    g_option_context_add_main_entries (context, entries, "shotwell");
+    if (g_option_context_parse (context, &argc, &argv, &error) == FALSE) {
+        g_print ("Failed to parse options: %s\n", error->message);
+        exit(1);
+    }
+
+    loop = g_main_loop_new (nullptr, FALSE);
+
+
+    // We are running on the session bus
+    if (address == nullptr) {
+        g_debug("Starting %s on G_BUS_TYPE_SESSION", argv[0]);
+        g_bus_own_name(G_BUS_TYPE_SESSION, FACEDETECT_INTERFACE_NAME.data(), G_BUS_NAME_OWNER_FLAGS_NONE,
+                nullptr, on_name_acquired, on_name_lost, loop, nullptr);
+
+    } else {
+        g_debug("Starting %s on %s", argv[0], address);
+        GDBusAuthObserver *observer = g_dbus_auth_observer_new ();
+        g_signal_connect (G_OBJECT (observer), "authorize-authenticated-peer",
+                G_CALLBACK (on_authorize_authenticated_peer), nullptr);
+
+        GDBusConnection *connection = g_dbus_connection_new_for_address_sync (address,
+                                                             G_DBUS_CONNECTION_FLAGS_AUTHENTICATION_CLIENT,
+                                                             observer,
+                                                             nullptr,
+                                                             &error);
+        if (connection != nullptr) {
+            on_name_acquired (connection, FACEDETECT_INTERFACE_NAME.data (), loop);
+        }
+    }
+
+    if (error != nullptr) {
+        g_error("Failed to get connection on %s bus: %s",
+                address == nullptr ? "session" : "private",
+                error->message);
+    }
+
+    g_main_loop_run (loop);
+    return 0;
+}
diff --git a/subprojects/shotwell-facedetect/shotwell-facedetect.hpp b/subprojects/shotwell-facedetect/shotwell-facedetect.hpp
new file mode 100644
index 0000000..abb5a65
--- /dev/null
+++ b/subprojects/shotwell-facedetect/shotwell-facedetect.hpp
@@ -0,0 +1,33 @@
+/* 
+ * Copyright 2018 Narendra A (narendra_m_a(at)yahoo dot com)
+ * 
+ * This software is licensed under the GNU LGPL (version 2.1 or later).
+ * See the COPYING file in this distribution.
+ *
+ * Header file for facedetect/recognition routines
+ */
+
+#pragma once
+
+#include <opencv2/core/core.hpp>
+
+#include <gio/gio.h>
+
+#include <vector>
+
+struct FaceRect {
+    FaceRect()
+      : vec(128, 0)
+    {
+    }
+    float x{ 0.0F };
+    float y{ 0.0F };
+    float width{ 0.0F };
+    float height{ 0.0F };
+    std::vector<double> vec;
+
+    GVariant *serialize() const;
+};
+
+bool loadNet(const cv::String& netFile);
+std::vector<FaceRect> detectFaces(const cv::String& inputName, double scale, bool infer);