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+---
+layout: default
+title: Face Detection
+parent: Solutions
+nav_order: 1
+---
+
+# MediaPipe Face Detection
+{: .no_toc }
+
+1. TOC
+{:toc}
+---
+
+## Overview
+
+MediaPipe Face Detection is an ultrafast face detection solution that comes with
+6 landmarks and multi-face support. It is based on
+[BlazeFace](https://arxiv.org/abs/1907.05047), 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](./face_mesh.md)), facial features or expression
+classification, and face region segmentation. BlazeFace uses a lightweight
+feature extraction network inspired by, but distinct from
+[MobileNetV1/V2](https://ai.googleblog.com/2018/04/mobilenetv2-next-generation-of-on.html),
+a GPU-friendly anchor scheme modified from
+[Single Shot MultiBox Detector (SSD)](https://arxiv.org/abs/1512.02325), and an
+improved tie resolution strategy alternative to non-maximum suppression. For
+more information about BlazeFace, please see the [Resources](#resources)
+section.
+
+![face_detection_android_gpu.gif](../images/mobile/face_detection_android_gpu.gif)
+
+## Example Apps
+
+Please first see general instructions for
+[Android](../getting_started/building_examples.md#android), [iOS](../getting_started/building_examples.md#ios)
+and [desktop](../getting_started/building_examples.md#desktop) on how to build MediaPipe
+examples.
+
+Note: To visualize a graph, copy the graph and paste it into
+[MediaPipe Visualizer](https://viz.mediapipe.dev/). For more information on how
+to visualize its associated subgraphs, please see
+[visualizer documentation](../visualizer.md).
+
+### Mobile
+
+#### GPU Pipeline
+
+*   Graph:
+    [`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)
+*   Android target:
+    [(or download prebuilt ARM64 APK)](https://drive.google.com/open?id=1DZTCy1gp238kkMnu4fUkwI3IrF77Mhy5)
+    [`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)
+*   iOS target:
+    [`mediapipe/examples/ios/facedetectiongpu:FaceDetectionGpuApp`](https://github.com/google/mediapipe/tree/master/mediapipe/examples/ios/facedetectiongpu/BUILD)
+
+#### CPU Pipeline
+
+This is very similar to the [GPU pipeline](#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.
+
+*   Graph:
+    [`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)
+*   Android target:
+    [(or download prebuilt ARM64 APK)](https://drive.google.com/open?id=1npiZY47jbO5m2YaL63o5QoCQs40JC6C7)
+    [`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)
+*   iOS target:
+    [`mediapipe/examples/ios/facedetectioncpu:FaceDetectionCpuApp`](https://github.com/google/mediapipe/tree/master/mediapipe/examples/ios/facedetectioncpu/BUILD)
+
+### Desktop
+
+*   Running on CPU:
+    *   Graph:
+        [`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)
+    *   Target:
+        [`mediapipe/examples/desktop/face_detection:face_detection_cpu`](https://github.com/google/mediapipe/tree/master/mediapipe/examples/desktop/face_detection/BUILD)
+*   Running on GPU
+    *   Graph:
+        [`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)
+    *   Target:
+        [`mediapipe/examples/desktop/face_detection:face_detection_gpu`](https://github.com/google/mediapipe/tree/master/mediapipe/examples/desktop/face_detection/BUILD)
+
+### Web
+
+Please refer to [these instructions](../index.md#mediapipe-on-the-web).
+
+### Coral
+
+Please refer to
+[these instructions](https://github.com/google/mediapipe/tree/master/mediapipe/examples/coral/README.md)
+to cross-compile and run MediaPipe examples on the
+[Coral Dev Board](https://coral.ai/products/dev-board).
+
+## Resources
+
+*   Paper:
+    [BlazeFace: Sub-millisecond Neural Face Detection on Mobile GPUs](https://arxiv.org/abs/1907.05047)
+    ([presentation](https://docs.google.com/presentation/d/1YCtASfnYyZtH-41QvnW5iZxELFnf0MF-pPWSLGj8yjQ/present?slide=id.g5bc8aeffdd_1_0))
+    ([poster](https://drive.google.com/file/d/1u6aB6wxDY7X2TmeUUKgFydulNtXkb3pu/view))
+*   For front-facing/selfie camera:
+    [TFLite model](https://github.com/google/mediapipe/tree/master/mediapipe/models/face_detection_front.tflite),
+    [TFLite model quantized for EdgeTPU/Coral](https://github.com/google/mediapipe/tree/master/mediapipe/examples/coral/models/face-detector-quantized_edgetpu.tflite)
+*   For back-facing camera:
+    [TFLite model ](https://github.com/google/mediapipe/tree/master/mediapipe/models/face_detection_back.tflite)
+*   [Model card](https://drive.google.com/file/d/1f39lSzU5Oq-j_OXgS67KfN5wNsoeAZ4V/view)