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MediaPipe Face Detection

  1. Overview
  2. Example Apps
    1. Mobile
      1. GPU Pipeline
      2. CPU Pipeline
    2. Desktop
    3. Web
    4. Coral
  3. Resources

Overview

MediaPipe Face Detection is an ultrafast face detection solution that comes with 6 landmarks and multi-face support. It is based on BlazeFace, a lightweight and well-performing face detector tailored for mobile GPU inference. The detector’s super-realtime performance enables it to be applied to any live viewfinder experience that requires an accurate facial region of interest as an input for other task-specific models, such as 3D facial keypoint or geometry estimation (e.g., MediaPipe Face Mesh), facial features or expression classification, and face region segmentation. BlazeFace uses a lightweight feature extraction network inspired by, but distinct from MobileNetV1/V2, a GPU-friendly anchor scheme modified from Single Shot MultiBox Detector (SSD), and an improved tie resolution strategy alternative to non-maximum suppression. For more information about BlazeFace, please see the Resources section.

face_detection_android_gpu.gif

Example Apps

Please first see general instructions for Android, iOS and desktop on how to build MediaPipe examples.

Note: To visualize a graph, copy the graph and paste it into MediaPipe Visualizer. For more information on how to visualize its associated subgraphs, please see visualizer documentation.

Mobile

GPU Pipeline

CPU Pipeline

This is very similar to the GPU pipeline except that at the beginning and the end of the pipeline it performs GPU-to-CPU and CPU-to-GPU image transfer respectively. As a result, the rest of graph, which shares the same configuration as the GPU pipeline, runs entirely on CPU.

Desktop

Web

Please refer to these instructions.

Coral

Please refer to these instructions to cross-compile and run MediaPipe examples on the Coral Dev Board.

Resources