Detect Faces with ML Kit on Android

You can use ML Kit to detect faces in images and video.

See the ML Kit quickstart sample on GitHub for an example of this API in use.

Before you begin

  1. If you have not already added Firebase to your app, do so by following the steps in the getting started guide.
  2. Include the dependencies for ML Kit in your app-level build.gradle file:
    dependencies {
      // ...
      implementation ''
      implementation ''
  3. Optional but recommended: Configure your app to automatically download the ML model to the device after your app is installed from the Play Store.

    To do so, add the following declaration to your app's AndroidManifest.xml file:

    <application ...>
          android:value="face" />
      <!-- To use multiple models: android:value="face,model2,model3" -->
    If you do not enable install-time model downloads, the model will be downloaded the first time you run the detector. Requests you make before the download has completed will produce no results.

Input image guidelines

For ML Kit to accurately detect faces, input images must contain faces that are represented by sufficient pixel data. In general, each face you want to detect in an image should be at least 100x100 pixels. If you want to detect the contours of faces, ML Kit requires higher resolution input: each face should be at least 200x200 pixels.

If you are detecting faces in a real-time application, you might also want to consider the overall dimensions of the input images. Smaller images can be processed faster, so to reduce latency, capture images at lower resolutions (keeping in mind the above accuracy requirements) and ensure that the subject's face occupies as much of the image as possible. Also see Tips to improve real-time performance.

Poor image focus can hurt accuracy. If you aren't getting acceptable results, try asking the user to recapture the image.

The orientation of a face relative to the camera can also affect what facial features ML Kit detects. See Face Detection Concepts.

1. Configure the face detector

Before you apply face detection to an image, if you want to change any of the face detector's default settings, specify those settings with a FirebaseVisionFaceDetectorOptions object. You can change the following settings:

Performance mode FAST (default) | ACCURATE

Favor speed or accuracy when detecting faces.

Detect landmarks NO_LANDMARKS (default) | ALL_LANDMARKS

Whether to attempt to identify facial "landmarks": eyes, ears, nose, cheeks, mouth, and so on.

Detect contours NO_CONTOURS (default) | ALL_CONTOURS

Whether to detect the contours of facial features. Contours can be detected for up to 5 faces per image.


Whether or not to classify faces into categories such as "smiling", and "eyes open".

Minimum face size float (default: 0.1f)

The minimum size, relative to the image, of faces to detect.

Enable face tracking false (default) | true

Whether or not to assign faces an ID, which can be used to track faces across images.

For example:

// High-accuracy landmark detection and face classification
FirebaseVisionFaceDetectorOptions options =
    new FirebaseVisionFaceDetectorOptions.Builder()

// Real-time contour detection of multiple faces
FirebaseVisionFaceDetectorOptions options =
    new FirebaseVisionFaceDetectorOptions.Builder()

2. Run the face detector

To detect faces in an image, create a FirebaseVisionImage object from either a Bitmap, media.Image, ByteBuffer, byte array, or a file on the device. Then, pass the FirebaseVisionImage object to the FirebaseVisionFaceDetector's detectInImage method.

For face recognition, you should use an image with dimensions of at least 480x360 pixels. If you are recognizing faces in real time, capturing frames at this minimum resolution can help reduce latency.

  1. Create a FirebaseVisionImage object from your image.

    • To create a FirebaseVisionImage object from a Bitmap object:
      FirebaseVisionImage image = FirebaseVisionImage.fromBitmap(bitmap);
      The image represented by the Bitmap object must be upright, with no additional rotation required.
    • To create a FirebaseVisionImage object from a media.Image object, such as when capturing an image from a device's camera, first determine the angle the image must be rotated to compensate for both the device's rotation and the orientation of camera sensor in the device:
      private static final SparseIntArray ORIENTATIONS = new SparseIntArray();
      static {
          ORIENTATIONS.append(Surface.ROTATION_0, 90);
          ORIENTATIONS.append(Surface.ROTATION_90, 0);
          ORIENTATIONS.append(Surface.ROTATION_180, 270);
          ORIENTATIONS.append(Surface.ROTATION_270, 180);
       * Get the angle by which an image must be rotated given the device's current
       * orientation.
      @RequiresApi(api = Build.VERSION_CODES.LOLLIPOP)
      private int getRotationCompensation(String cameraId, Activity activity, Context context)
              throws CameraAccessException {
          // Get the device's current rotation relative to its "native" orientation.
          // Then, from the ORIENTATIONS table, look up the angle the image must be
          // rotated to compensate for the device's rotation.
          int deviceRotation = activity.getWindowManager().getDefaultDisplay().getRotation();
          int rotationCompensation = ORIENTATIONS.get(deviceRotation);
          // On most devices, the sensor orientation is 90 degrees, but for some
          // devices it is 270 degrees. For devices with a sensor orientation of
          // 270, rotate the image an additional 180 ((270 + 270) % 360) degrees.
          CameraManager cameraManager = (CameraManager) context.getSystemService(CAMERA_SERVICE);
          int sensorOrientation = cameraManager
          rotationCompensation = (rotationCompensation + sensorOrientation + 270) % 360;
          // Return the corresponding FirebaseVisionImageMetadata rotation value.
          int result;
          switch (rotationCompensation) {
              case 0:
                  result = FirebaseVisionImageMetadata.ROTATION_0;
              case 90:
                  result = FirebaseVisionImageMetadata.ROTATION_90;
              case 180:
                  result = FirebaseVisionImageMetadata.ROTATION_180;
              case 270:
                  result = FirebaseVisionImageMetadata.ROTATION_270;
                  result = FirebaseVisionImageMetadata.ROTATION_0;
                  Log.e(TAG, "Bad rotation value: " + rotationCompensation);
          return result;

      Then, pass the media.Image object and the rotation value to FirebaseVisionImage.fromMediaImage():

      FirebaseVisionImage image = FirebaseVisionImage.fromMediaImage(mediaImage, rotation);
    • To create a FirebaseVisionImage object from a ByteBuffer or a byte array, first calculate the image rotation as described above.

      Then, create a FirebaseVisionImageMetadata object that contains the image's height, width, color encoding format, and rotation:

      FirebaseVisionImageMetadata metadata = new FirebaseVisionImageMetadata.Builder()
              .setWidth(480)   // 480x360 is typically sufficient for
              .setHeight(360)  // image recognition

      Use the buffer or array, and the metadata object, to create a FirebaseVisionImage object:

      FirebaseVisionImage image = FirebaseVisionImage.fromByteBuffer(buffer, metadata);
      // Or: FirebaseVisionImage image = FirebaseVisionImage.fromByteArray(byteArray, metadata);
    • To create a FirebaseVisionImage object from a file, pass the app context and file URI to FirebaseVisionImage.fromFilePath():
      FirebaseVisionImage image;
      try {
          image = FirebaseVisionImage.fromFilePath(context, uri);
      } catch (IOException e) {
  2. Get an instance of FirebaseVisionFaceDetector:

    FirebaseVisionFaceDetector detector = FirebaseVision.getInstance()
  3. Finally, pass the image to the detectInImage method:

    Task<List<FirebaseVisionFace>> result =
                            new OnSuccessListener<List<FirebaseVisionFace>>() {
                                public void onSuccess(List<FirebaseVisionFace> faces) {
                                    // Task completed successfully
                                    // ...
                            new OnFailureListener() {
                                public void onFailure(@NonNull Exception e) {
                                    // Task failed with an exception
                                    // ...

3. Get information about detected faces

If the face recognition operation succeeds, a list of FirebaseVisionFace objects will be passed to the success listener. Each FirebaseVisionFace object represents a face that was detected in the image. For each face, you can get its bounding coordinates in the input image, as well as any other information you configured the face detector to find. For example:

for (FirebaseVisionFace face : faces) {
    Rect bounds = face.getBoundingBox();
    float rotY = face.getHeadEulerAngleY();  // Head is rotated to the right rotY degrees
    float rotZ = face.getHeadEulerAngleZ();  // Head is tilted sideways rotZ degrees

    // If landmark detection was enabled (mouth, ears, eyes, cheeks, and
    // nose available):
    FirebaseVisionFaceLandmark leftEar = face.getLandmark(FirebaseVisionFaceLandmark.LEFT_EAR);
    if (leftEar != null) {
        FirebaseVisionPoint leftEarPos = leftEar.getPosition();

    // If contour detection was enabled:
    List<FirebaseVisionPoint> leftEyeContour =
    List<FirebaseVisionPoint> upperLipBottomContour =

    // If classification was enabled:
    if (face.getSmilingProbability() != FirebaseVisionFace.UNCOMPUTED_PROBABILITY) {
        float smileProb = face.getSmilingProbability();
    if (face.getRightEyeOpenProbability() != FirebaseVisionFace.UNCOMPUTED_PROBABILITY) {
        float rightEyeOpenProb = face.getRightEyeOpenProbability();

    // If face tracking was enabled:
    if (face.getTrackingId() != FirebaseVisionFace.INVALID_ID) {
        int id = face.getTrackingId();

Example of face contours

When you have face contour detection enabled, you get a list of points for each facial feature that was detected. These points represent the shape of the feature. See the Face Detection Concepts Overview for details about how contours are represented.

The following image illustrates how these points map to a face (click the image to enlarge):

Real-time face detection

If you want to use face detection in a real-time application, follow these guidelines to achieve the best framerates:

  • Configure the face detector to use either face contour detection or classification and landmark detection, but not both:

    Contour detection
    Landmark detection
    Landmark detection and classification
    Contour detection and landmark detection
    Contour detection and classification
    Contour detection, landmark detection, and classification

  • Enable FAST mode (enabled by default).

  • Consider capturing images at a lower resolution. However, also keep in mind this API's image dimension requirements.

  • Throttle calls to the detector. If a new video frame becomes available while the detector is running, drop the frame.
  • If you are using the output of the detector to overlay graphics on the input image, first get the result from ML Kit, then render the image and overlay in a single step. By doing so, you render to the display surface only once for each input frame. See the CameraSourcePreview and GraphicOverlay classes in the quickstart sample app for an example.
  • If you use the Camera2 API, capture images in ImageFormat.YUV_420_888 format.

    If you use the older Camera API, capture images in ImageFormat.NV21 format.

Send feedback about...

Need help? Visit our support page.