Wolfram Research

ResNet-101 for 3D Morphable Model Regression Trained on Casia WebFace Data

Represent a facial image as a vector

Released in 2016, this model takes a facial image as input and produces a 198-dimensional feature vector representing its 3D morphable model. The feature vector produced by the net is meant to be consumed by the Basel Face Model; the first half of the vector (the first 99 components) represents the 3D shape while the second represents texture. The model can be effectively used as a generic feature extractor.

Number of layers: 346 | Parameter count: 43,011,206 | Trained size: 174 MB |

Training Set Information

Performance

Examples

Resource retrieval

Get the pre-trained net:

In[1]:=
NetModel["ResNet-101 for 3D Morphable Model Regression Trained on \
Casia WebFace Data"]
Out[1]=

Basic usage

Compute a feature vector for a given image:

In[2]:=
features = 
  NetModel["ResNet-101 for 3D Morphable Model Regression Trained on \
Casia WebFace Data"][\!\(\*
GraphicsBox[
TagBox[RasterBox[CompressedData["
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xyo=
"], {{0, 75}, {75, 0}}, {0, 255},
ColorFunction->RGBColor],
BoxForm`ImageTag[
       "Byte", ColorSpace -> "RGB", Interleaving -> True, 
        MetaInformation -> Association[
         "Source" -> "http://wiki.d-addicts.com/File:RaymondLam.jpg", 
          "URL" -> "http://www.wolframcdn.com/waimage/hset028/891/\
8918eb5845add22945c22846d797ed18_v001s.jpg"]],
Selectable->False],
DefaultBaseStyle->"ImageGraphics",
ImageSizeRaw->{75, 75},
PlotRange->{{0, 75}, {0, 75}}]\)];

Get the length of the feature vector:

In[3]:=
Length[features]
Out[3]=

Use a batch of face images:

In[4]:=
(* Evaluate this cell to get the example input *) CloudGet["https://www.wolframcloud.com/obj/20a313b1-7ae5-4ce1-bfc7-4dfb6f7306be"]

Compute the feature vectors for the batch of images and obtain the dimensions of the features:

In[5]:=
features = 
  NetModel["ResNet-101 for 3D Morphable Model Regression Trained on \
Casia WebFace Data"][faces];
In[6]:=
Dimensions[features]
Out[6]=

Reduce the feature vectors to two dimensions with t-SNE:

In[7]:=
points = DimensionReduce[features, 2, Method -> "TSNE"];
In[8]:=
Dimensions[points]
Out[8]=

Visualize the results:

In[9]:=
Graphics[
 MapThread[Inset[#1, #2, {0, 0}, 0.5] &, {faces, points}],
 ImageSize -> 600, ImagePadding -> 25]
Out[9]=

Obtain the five closest faces to a given one:

In[10]:=
FeatureNearest[faces, \!\(\*
GraphicsBox[
TagBox[RasterBox[CompressedData["
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"], {{0, 50.}, {50., 0}}, {0, 255},
ColorFunction->RGBColor],
BoxForm`ImageTag[
     "Byte", ColorSpace -> ColorProfileData[CompressedData["
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"], "RGB", "XYZ"], 
      Interleaving -> True, 
      MetaInformation -> Association[
       "Source" -> "http://commons.wikimedia.org/wiki/File:Amanda_\
Ragan_-_Official_Portrait_-_84th_GA.jpg", 
        "URL" -> "http://www.wolframcdn.com/waimage/hset028/69f/\
69f5125f69baa5295fd856e67a31f861_v001s.jpg"]],
Selectable->False],
DefaultBaseStyle->"ImageGraphics",
ImageSizeRaw->{50., 50.},
PlotRange->{{0, 50.}, {0, 50.}}]\), 5, 
 FeatureExtractor -> 
  NetModel["ResNet-101 for 3D Morphable Model Regression Trained on \
Casia WebFace Data"], DistanceFunction -> CosineDistance]
Out[10]=

Net information

Inspect the number of parameters of all arrays in the net:

In[11]:=
Information[
 NetModel["ResNet-101 for 3D Morphable Model Regression Trained on \
Casia WebFace Data"], "ArraysElementCounts"]
Out[11]=

Obtain the total number of parameters:

In[12]:=
Information[
 NetModel["ResNet-101 for 3D Morphable Model Regression Trained on \
Casia WebFace Data"], "ArraysTotalElementCount"]
Out[12]=

Obtain the layer type counts:

In[13]:=
Information[
 NetModel["ResNet-101 for 3D Morphable Model Regression Trained on \
Casia WebFace Data"], "LayerTypeCounts"]
Out[13]=

Display the summary graphic:

In[14]:=
Information[
 NetModel["ResNet-101 for 3D Morphable Model Regression Trained on \
Casia WebFace Data"], "SummaryGraphic"]
Out[14]=

Export to MXNet

Export the net into a format that can be opened in MXNet:

In[15]:=
jsonPath = 
 Export[FileNameJoin[{$TemporaryDirectory, "net.json"}], 
  NetModel["ResNet-101 for 3D Morphable Model Regression Trained on \
Casia WebFace Data"], "MXNet"]
Out[15]=

Export also creates a net.params file containing parameters:

In[16]:=
paramPath = FileNameJoin[{DirectoryName[jsonPath], "net.params"}]
Out[16]=

Get the size of the parameter file:

In[17]:=
FileByteCount[paramPath]
Out[17]=

The size is similar to the byte count of the resource object:

In[18]:=
NetModel["ResNet-101 for 3D Morphable Model Regression Trained on \
Casia WebFace Data", "ByteCount"]
Out[18]=

Requirements

Wolfram Language 12.1 (March 2020) or above

Resource History

Reference