Face detection guide for Android

The MediaPipe Face Detector task lets you detect faces in an image or video. You can use this task to locate faces and facial features within a frame. This task uses a machine learning (ML) model that works with single images or a continuous stream of images. The task outputs face locations, along with the following facial key points: left eye, right eye, nose tip, mouth, left eye tragion, and right eye tragion.

The code sample described in these instructions is available on GitHub. For more information about the capabilities, models, and configuration options of this task, see the Overview.

Code example

The MediaPipe Tasks example code is a simple implementation of a Face Detector app for Android. The example uses the camera on a physical Android device to detect faces in a continuous video stream. The app can also detect faces in images and videos from the device gallery.

You can use the app as a starting point for your own Android app, or refer to it when modifying an existing app. The Face Detector example code is hosted on GitHub.

Download the code

The following instructions show you how to create a local copy of the example code using the git command line tool.

Attention: This MediaPipe Solutions Preview is an early release. Learn more.

To download the example code:

1. Clone the git repository using the following command:

   git clone https://github.com/googlesamples/mediapipe
   

2. Optionally, configure your git instance to use sparse checkout, so you have only the files for the Face Detector example app:

   cd mediapipe
   git sparse-checkout init --cone
   git sparse-checkout set examples/face_detector/android
   

After creating a local version of the example code, you can import the project into Android Studio and run the app. For instructions, see the Setup Guide for Android.

Key components

The following files contain the crucial code for this face detection example application:

• FaceDetectorHelper.kt - Initializes the face detector and handles the model and delegate selection.
• CameraFragment.kt - Handles the device camera and processes the image and video input data.
• GalleryFragment.kt - Interacts with OverlayView to display the output image or video.
• OverlayView.kt - Implements the display with bounding boxes for detected faces.

Setup

This section describes key steps for setting up your development environment and code projects specifically to use Face Detector. For general information on setting up your development environment for using MediaPipe tasks, including platform version requirements, see the Setup guide for Android.

Attention: This MediaPipe Solutions Preview is an early release. Learn more.

Dependencies

The Face Detector task uses the com.google.mediapipe:tasks-vision library. Add this dependency to the build.gradle file of your Android app:

dependencies {
    implementation 'com.google.mediapipe:tasks-vision:latest.release'
}

Model

The MediaPipe Face Detector task requires a trained model bundle that is compatible with this task. For more information on available trained models for Face Detector, see the task overview Models section.

Select and download the model, and store it within your project directory:

<dev-project-root>/src/main/assets

Specify the path of the model within the ModelAssetPath parameter. In the example code, the model is defined in the FaceDetectorHelper.kt file:

val modelName = "face_detection_short_range.tflite"
baseOptionsBuilder.setModelAssetPath(modelName)

Create the task

The MediaPipe Face Detector task uses the createFromOptions() function to set up the task. The createFromOptions() function accepts values for the configuration options. For more information on configuration options, see Configuration options.

The Face Detector supports the following input data types: still images, video files, and live video streams. You need to specify the running mode corresponding to your input data type when creating the task. Choose the tab corresponding to your input data type to see how to create the task and run inference.

Image

val baseOptionsBuilder = BaseOptions.builder().setModelAssetPath(modelName)
val baseOptions = baseOptionBuilder.build()

val optionsBuilder =
    FaceDetector.FaceDetectorOptions.builder()
        .setBaseOptions(baseOptionsBuilder.build())
        .setMinDetectionConfidence(threshold)
        .setRunningMode(RunningMode.IMAGE)

val options = optionsBuilder.build()

FaceDetector =
    FaceDetector.createFromOptions(context, options)
    

Video

val baseOptionsBuilder = BaseOptions.builder().setModelAssetPath(modelName)
val baseOptions = baseOptionBuilder.build()

val optionsBuilder =
    FaceDetector.FaceDetectorOptions.builder()
        .setBaseOptions(baseOptionsBuilder.build())
        .setMinDetectionConfidence(threshold)
        .setRunningMode(RunningMode.VIDEO)

val options = optionsBuilder.build()

FaceDetector =
    FaceDetector.createFromOptions(context, options)
    

Live stream

val baseOptionsBuilder = BaseOptions.builder().setModelAssetPath(modelName)
val baseOptions = baseOptionBuilder.build()

val optionsBuilder =
    FaceDetector.FaceDetectorOptions.builder()
        .setBaseOptions(baseOptionsBuilder.build())
        .setMinDetectionConfidence(threshold)
        .setResultListener(this::returnLivestreamResult)
        .setErrorListener(this::returnLivestreamError)
        .setRunningMode(RunningMode.LIVE_STREAM)

val options = optionsBuilder.build()

FaceDetector =
    FaceDetector.createFromOptions(context, options)
    

Note: If you use the live stream mode, you’ll need to register a result listener when creating the task. The task calls the listener when it finishes processing a video frame, with the detection result and the input image as parameters.

Note: If you use the video mode or live stream mode, Face Detector uses tracking to avoid triggering palm detection model on every frame, which helps reduce latency.

The Face Detector example code implementation allows the user to switch between processing modes. The approach makes the task creation code more complicated and may not be appropriate for your use case. You can see this code in the setupFaceDetector() function in the FaceDetectorHelper.kt file.

Configuration options

This task has the following configuration options for Android apps:

Option Name	Description	Value Range	Default Value
runningMode	Sets the running mode for the task. There are three modes: IMAGE: The mode for single image inputs. VIDEO: The mode for decoded frames of a video. LIVE_STREAM: The mode for a livestream of input data, such as from a camera. In this mode, resultListener must be called to set up a listener to receive results asynchronously.	{IMAGE, VIDEO, LIVE_STREAM}	IMAGE
minDetectionConfidence	The minimum confidence score for the face detection to be considered successful.	Float [0,1]	0.5
minSuppressionThreshold	The minimum non-maximum-suppression threshold for face detection to be considered overlapped.	Float [0,1]	0.3
resultListener	Sets the result listener to receive the detection results asynchronously when the Face Detector is in the live stream mode. Can only be used when running mode is set to LIVE_STREAM.	N/A	Not set
errorListener	Sets an optional error listener.	N/A	Not set

Prepare data

Face Detector works with images, video file and live video streams. The task handles the data input preprocessing, including resizing, rotation and value normalization.

The following code demonstrates how to hand off data for processing. These samples include details on how to handle data from images, video files, and live video streams.

Image

import com.google.mediapipe.framework.image.BitmapImageBuilder
import com.google.mediapipe.framework.image.MPImage

// Convert the input Bitmap object to an MPImage object to run inference
val mpImage = BitmapImageBuilder(image).build()
    

Video

import com.google.mediapipe.framework.image.BitmapImageBuilder
import com.google.mediapipe.framework.image.MPImage

val argb8888Frame =
    if (frame.config == Bitmap.Config.ARGB_8888) frame
    else frame.copy(Bitmap.Config.ARGB_8888, false)

// Convert the input Bitmap object to an MPImage object to run inference
val mpImage = BitmapImageBuilder(argb8888Frame).build()
    

Live stream

import com.google.mediapipe.framework.image.BitmapImageBuilder
import com.google.mediapipe.framework.image.MPImage

// Convert the input Bitmap object to an MPImage object to run inference
val mpImage = BitmapImageBuilder(rotatedBitmap).build()
    

In the Face Detector example code, the data preparation is handled in the FaceDetectorHelper.kt file.

Run the task

Depending on the type of data your are working with, use the faceDetector.detect...() method that is specific to that data type. Use detect() for individual images, detectForVideo() for frames in video files, and detectAsync() for video streams. When you are performing detections on a video stream, make sure you run the detections on a separate thread to avoid blocking the user interface thread.

The following code samples show simple examples of how to run Face Detector in these different data modes:

Image

val result = faceDetector.detect(mpImage)
    

Video

val timestampMs = i * inferenceIntervalMs

faceDetector.detectForVideo(mpImage, timestampMs)
    .let { detectionResult ->
        resultList.add(detectionResult)
    }
    

Live stream

val mpImage = BitmapImageBuilder(rotatedBitmap).build()
val frameTime = SystemClock.uptimeMillis()

faceDetector.detectAsync(mpImage, frameTime)
    

Note the following:

• When running in the video mode or the live stream mode, you must provide the timestamp of the input frame to the Face Detector task.
• When running in the image or the video mode, the Face Detector task blocks the current thread until it finishes processing the input image or frame. To avoid blocking the user interface, execute the processing in a background thread.
• When running in the live stream mode, the Face Detector task returns immediately and doesn’t block the current thread. It will invoke the result listener with the detection result every time it finishes processing an input frame. If the detection function is called when the Face Detector task is busy processing another frame, the task will ignore the new input frame.

In the Face Detector example code, the detect, detectForVideo, and detectAsync functions are defined in the FaceDetectorHelper.kt file.

Handle and display results

The Face Detector returns a FaceDetectorResult object for each detection run. The result object contains bounding boxes for the detected faces and a confidence score for each detected face.

The following shows an example of the output data from this task:

FaceDetectionResult:
  Detections:
    Detection #0:
      BoundingBox:
        origin_x: 126
        origin_y: 100
        width: 463
        height: 463
      Categories:
        Category #0:
          index: 0
          score: 0.9729152917861938
      NormalizedKeypoints:
        NormalizedKeypoint #0:
          x: 0.18298381567001343
          y: 0.2961040139198303
        NormalizedKeypoint #1:
          x: 0.3302789330482483
          y: 0.29289937019348145
        ... (6 keypoints for each face)
    Detection #1:
      BoundingBox:
        origin_x: 616
        origin_y: 193
        width: 430
        height: 430
      Categories:
        Category #0:
          index: 0
          score: 0.9251380562782288
      NormalizedKeypoints:
        NormalizedKeypoint #0:
          x: 0.6151331663131714
          y: 0.3713381886482239
        NormalizedKeypoint #1:
          x: 0.7460576295852661
          y: 0.38825345039367676
        ... (6 keypoints for each face)

The following image shows a visualization of the task output:

For the image without bounding boxes, see the original image.

The Face Detector example code demonstrates how to display the results returned from the task, see the OverlayView class for more details.