111 lines
5.6 KiB
Markdown
111 lines
5.6 KiB
Markdown
---
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layout: default
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title: Face Detection
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parent: Solutions
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nav_order: 1
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---
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# MediaPipe Face Detection
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{: .no_toc }
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1. TOC
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{:toc}
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---
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## Overview
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MediaPipe Face Detection is an ultrafast face detection solution that comes with
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6 landmarks and multi-face support. It is based on
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[BlazeFace](https://arxiv.org/abs/1907.05047), a lightweight and well-performing
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face detector tailored for mobile GPU inference. The detector's super-realtime
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performance enables it to be applied to any live viewfinder experience that
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requires an accurate facial region of interest as an input for other
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task-specific models, such as 3D facial keypoint or geometry estimation (e.g.,
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[MediaPipe Face Mesh](./face_mesh.md)), facial features or expression
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classification, and face region segmentation. BlazeFace uses a lightweight
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feature extraction network inspired by, but distinct from
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[MobileNetV1/V2](https://ai.googleblog.com/2018/04/mobilenetv2-next-generation-of-on.html),
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a GPU-friendly anchor scheme modified from
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[Single Shot MultiBox Detector (SSD)](https://arxiv.org/abs/1512.02325), and an
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improved tie resolution strategy alternative to non-maximum suppression. For
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more information about BlazeFace, please see the [Resources](#resources)
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section.
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## Example Apps
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Please first see general instructions for
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[Android](../getting_started/building_examples.md#android), [iOS](../getting_started/building_examples.md#ios)
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and [desktop](../getting_started/building_examples.md#desktop) on how to build MediaPipe
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examples.
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Note: To visualize a graph, copy the graph and paste it into
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[MediaPipe Visualizer](https://viz.mediapipe.dev/). For more information on how
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to visualize its associated subgraphs, please see
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[visualizer documentation](../tools/visualizer.md).
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### Mobile
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#### GPU Pipeline
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* Graph:
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[`mediapipe/graphs/face_detection/face_detection_mobile_gpu.pbtxt`](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/face_detection/face_detection_mobile_gpu.pbtxt)
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* Android target:
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[(or download prebuilt ARM64 APK)](https://drive.google.com/open?id=1DZTCy1gp238kkMnu4fUkwI3IrF77Mhy5)
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[`mediapipe/examples/android/src/java/com/google/mediapipe/apps/facedetectiongpu:facedetectiongpu`](https://github.com/google/mediapipe/tree/master/mediapipe/examples/android/src/java/com/google/mediapipe/apps/facedetectiongpu/BUILD)
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* iOS target:
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[`mediapipe/examples/ios/facedetectiongpu:FaceDetectionGpuApp`](https://github.com/google/mediapipe/tree/master/mediapipe/examples/ios/facedetectiongpu/BUILD)
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#### CPU Pipeline
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This is very similar to the [GPU pipeline](#gpu-pipeline) except that at the
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beginning and the end of the pipeline it performs GPU-to-CPU and CPU-to-GPU
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image transfer respectively. As a result, the rest of graph, which shares the
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same configuration as the GPU pipeline, runs entirely on CPU.
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* Graph:
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[`mediapipe/graphs/face_detection/face_detection_mobile_cpu.pbtxt`](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/face_detection/face_detection_mobile_cpu.pbtxt)
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* Android target:
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[(or download prebuilt ARM64 APK)](https://drive.google.com/open?id=1npiZY47jbO5m2YaL63o5QoCQs40JC6C7)
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[`mediapipe/examples/android/src/java/com/google/mediapipe/apps/facedetectioncpu:facedetectioncpu`](https://github.com/google/mediapipe/tree/master/mediapipe/examples/android/src/java/com/google/mediapipe/apps/facedetectioncpu/BUILD)
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* iOS target:
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[`mediapipe/examples/ios/facedetectioncpu:FaceDetectionCpuApp`](https://github.com/google/mediapipe/tree/master/mediapipe/examples/ios/facedetectioncpu/BUILD)
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### Desktop
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* Running on CPU:
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* Graph:
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[`mediapipe/graphs/face_detection/face_detection_desktop_live.pbtxt`](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/face_detection/face_detection_desktop_live.pbtxt)
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* Target:
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[`mediapipe/examples/desktop/face_detection:face_detection_cpu`](https://github.com/google/mediapipe/tree/master/mediapipe/examples/desktop/face_detection/BUILD)
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* Running on GPU
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* Graph:
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[`mediapipe/graphs/face_detection/face_detection_mobile_gpu.pbtxt`](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/face_detection/face_detection_mobile_gpu.pbtxt)
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* Target:
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[`mediapipe/examples/desktop/face_detection:face_detection_gpu`](https://github.com/google/mediapipe/tree/master/mediapipe/examples/desktop/face_detection/BUILD)
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### Web
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Please refer to [these instructions](../index.md#mediapipe-on-the-web).
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### Coral
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Please refer to
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[these instructions](https://github.com/google/mediapipe/tree/master/mediapipe/examples/coral/README.md)
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to cross-compile and run MediaPipe examples on the
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[Coral Dev Board](https://coral.ai/products/dev-board).
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## Resources
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* Paper:
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[BlazeFace: Sub-millisecond Neural Face Detection on Mobile GPUs](https://arxiv.org/abs/1907.05047)
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([presentation](https://docs.google.com/presentation/d/1YCtASfnYyZtH-41QvnW5iZxELFnf0MF-pPWSLGj8yjQ/present?slide=id.g5bc8aeffdd_1_0))
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([poster](https://drive.google.com/file/d/1u6aB6wxDY7X2TmeUUKgFydulNtXkb3pu/view))
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* For front-facing/selfie camera:
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[TFLite model](https://github.com/google/mediapipe/tree/master/mediapipe/models/face_detection_front.tflite),
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[TFLite model quantized for EdgeTPU/Coral](https://github.com/google/mediapipe/tree/master/mediapipe/examples/coral/models/face-detector-quantized_edgetpu.tflite)
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* For back-facing camera:
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[TFLite model ](https://github.com/google/mediapipe/tree/master/mediapipe/models/face_detection_back.tflite)
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* [Model card](https://mediapipe.page.link/blazeface-mc)
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