BlazeFace in CoreML

BlazeFace is a fast, light-weight face detector from Google Research. Read more, Paper on arXiv

A pretrained model is available as part of Google's MediaPipe framework.

Besides a bounding box, BlazeFace also predicts 6 keypoints for face landmarks (2x eyes, 2x ears, nose, mouth).

Because BlazeFace is designed for use on mobile devices, the pretrained model is in TFLite format. However, I wanted to use it in CoreML and not TensorFlow.

NOTE: The MediaPipe model is slightly different from the model described in the BlazeFace paper. It uses depthwise convolutions with a 3x3 kernel, not 5x5. And it only uses "single" BlazeBlocks, not "double" ones.

The BlazePaper paper mentions that there are two versions of the model, one for the front-facing camera and one for the back-facing camera. This repo includes only the frontal camera model, as that is the only one I was able to find an official trained version for. The difference between the two models is the dataset they were trained on. As the paper says,

For the frontal camera model, only faces that occupy more than 20% of the image area were considered due to the intended use case (the threshold for the rear-facing camera model was 5%).

This means the included model will not be able to detect faces that are relatively small. It's really intended for selfies, not for general-purpose face detection.

Inside this repo

Essential ML files:

• ML/blazeface.py: defines the BlazeFace class that does all the work

• ML/blazeface.pth: the weights for the trained model

• ML/anchors.npy: lookup table with anchor boxes

ML Notebooks:

• ML/GenAnchors.ipynb: creates anchor boxes and saves them as a binary file (ML/anchors.npy)

• ML/Convert2PyTorch.ipynb: loads the weights from the TFLite model and converts them to PyTorch format (ML/blazeface.pth)

• ML/InferencePyTorch.ipynb: shows how to use the BlazeFace class to make face detections

• ML/coremlconv.py: loads the weights from the PyTorch model and converts them to CoreML format

iOS CoreML App

• App/

Detections

CoreML

Each face detection has 8 SIMD2 vectors(landmarks) and 1 Float number(confidence):

• The first 4 numbers describe the bounding box corners:

  • xmin, ymin, xmax, ymax

• The next 12 numbers are the x,y-coordinates of the 6 facial landmark keypoints:

  • right_eye_x, right_eye_y
  • left_eye_x, left_eye_y
  • nose_x, nose_y
  • mouth_x, mouth_y
  • right_ear_x, right_ear_y
  • left_ear_x, left_ear_y
  • Tip: these labeled as seen from the perspective of the person, so their right is your left.

• These are normalized coordinates (between 0 and 1).

• SIMD2 is used for faster vector math.

• The final number is the confidence score that this detection really is a face.

PyTorch

Each face detection is a PyTorch Tensor consisting of 17 numbers:

• The first 4 numbers describe the bounding box corners:

  • ymin, xmin, ymax, xmax
  • These are normalized coordinates (between 0 and 1).

• The next 12 numbers are the x,y-coordinates of the 6 facial landmark keypoints:

  • right_eye_x, right_eye_y
  • left_eye_x, left_eye_y
  • nose_x, nose_y
  • mouth_x, mouth_y
  • right_ear_x, right_ear_y
  • left_ear_x, left_ear_y
  • Tip: these labeled as seen from the perspective of the person, so their right is your left.

• The final number is the confidence score that this detection really is a face.

Image credits

Included for testing are the following images:

• ML/1face.png. Fei Fei Li by ITU Pictures, CC BY 2.0

• ML/3faces.png. Geoffrey Hinton, Yoshua Bengio, Yann Lecun. Found at AIBuilders

• ML/4faces.png from Andrew Ng’s Facebook page / KDnuggets

These images were scaled down to 128x128 pixels as that is the expected input size of the model.