Wolfram Computation Meets Knowledge

OpenFace Face Recognition Net Trained on CASIA-WebFace and FaceScrub Data

Represent a facial image as a vector

Released in 2015, this facial feature extractor, based on the Inception architecture, was trained to learn a mapping directly from facial images to 128-dimensional feature vectors. Training was performed using triplets of examples, with two examples belonging to the same person. Facial images sharing the same identity are optimized to be embedded close to each other in the feature space.

Number of layers: 172 | Parameter count: 3,743,280 | Trained size: 15 MB

Training Set Information

Performance

Examples

Resource retrieval

Retrieve the resource object:

In[1]:=
ResourceObject["OpenFace Face Recognition Net Trained on \
CASIA-WebFace and FaceScrub Data"]
Out[1]=

Get the pre-trained net:

In[2]:=
NetModel["OpenFace Face Recognition Net Trained on CASIA-WebFace and \
FaceScrub Data"]
Out[2]=

Basic usage

Compute a feature vector for a given image:

In[3]:=
features = 
  NetModel["OpenFace Face Recognition Net Trained on CASIA-WebFace \
and FaceScrub Data"][\!\(\*
GraphicsBox[
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xyo=
"], {{0, 75}, {75, 0}}, {0, 255},
ColorFunction->RGBColor],
BoxForm`ImageTag[
       "Byte", ColorSpace -> "RGB", Interleaving -> True, 
        MetaInformation -> Association[
         "Source" -> "http://wiki.d-addicts.com/File:RaymondLam.jpg", 
          "URL" -> "http://www.wolframcdn.com/waimage/hset028/891/\
8918eb5845add22945c22846d797ed18_v001s.jpg"]],
Selectable->False],
DefaultBaseStyle->"ImageGraphics",
ImageSizeRaw->{75, 75},
PlotRange->{{0, 75}, {0, 75}}]\)];

Get the length of the feature vector:

In[4]:=
Length[features]
Out[4]=

Use a batch of face images:

In[5]:=
CloudGet["https://www.wolframcloud.com/objects/55ac41e3-97fd-41a8-958a-bbd63ad6f687"] (* Evaluate this cell to copy the example input from a cloud object *)

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

In[6]:=
features = 
  NetModel["OpenFace Face Recognition Net Trained on CASIA-WebFace \
and FaceScrub Data"][faces];
In[7]:=
Dimensions[features]
Out[7]=

Visualize the features in two and three dimensions:

In[8]:=
FeatureSpacePlot[faces, 
 FeatureExtractor -> 
  NetModel["OpenFace Face Recognition Net Trained on CASIA-WebFace \
and FaceScrub Data"], ImageSize -> Large]
Out[8]=
In[9]:=
FeatureSpacePlot3D[faces, 
 FeatureExtractor -> 
  NetModel["OpenFace Face Recognition Net Trained on CASIA-WebFace \
and FaceScrub Data"], ImageSize -> Large]
Out[9]=

Obtain the five closest faces to a given one:

In[10]:=
FeatureNearest[faces, \!\(\*
GraphicsBox[
TagBox[RasterBox[CompressedData["
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"], {{0, 50}, {50, 0}}, {0, 255},
ColorFunction->RGBColor],
BoxForm`ImageTag[
     "Byte", ColorSpace -> "RGB", Interleaving -> True, 
      MetaInformation -> Association[
       "Source" -> "http://en.wikipedia.org/wiki/File:Elizabeth_Clare_\
Prophet_-_Guru_Ma.JPG", 
        "URL" -> "http://www.wolframcdn.com/waimage/hset028/aeb/\
aeb98eda6645fcb390425ec1e451a972_v001s.jpg"]],
Selectable->False],
DefaultBaseStyle->"ImageGraphics",
ImageSizeRaw->{50, 50},
PlotRange->{{0, 50}, {0, 50}}]\), 5, 
 FeatureExtractor -> 
  NetModel["OpenFace Face Recognition Net Trained on CASIA-WebFace \
and FaceScrub Data"]]
Out[10]=

Net information

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

In[11]:=
NetInformation[
 NetModel["OpenFace Face Recognition Net Trained on CASIA-WebFace and \
FaceScrub Data"], "ArraysElementCounts"]
Out[11]=

Obtain the total number of parameters:

In[12]:=
NetInformation[
 NetModel["OpenFace Face Recognition Net Trained on CASIA-WebFace and \
FaceScrub Data"], "ArraysTotalElementCount"]
Out[12]=

Obtain the layer type counts:

In[13]:=
NetInformation[
 NetModel["OpenFace Face Recognition Net Trained on CASIA-WebFace and \
FaceScrub Data"], "LayerTypeCounts"]
Out[13]=

Display the summary graphic:

In[14]:=
NetInformation[
 NetModel["OpenFace Face Recognition Net Trained on CASIA-WebFace and \
FaceScrub 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["OpenFace Face Recognition Net Trained on CASIA-WebFace \
and FaceScrub 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]:=
ResourceObject[
  "OpenFace Face Recognition Net Trained on CASIA-WebFace and \
FaceScrub Data"]["ByteCount"]
Out[18]=

Requirements

Wolfram Language 11.3 (March 2018) or above

Resource History

Reference