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.

There are two ways to integrate face detection: a bundled model which is part of your app and an unbundled model that depends on Google Play Services. The two models are the same. If you select the unbundled model your app will be smaller.

FeatureUnbundledBundled
ImplementationModel is dynamically downloaded via Google Play Services.Model is statically linked to your app at build time.
App sizeNo impact.About 16MB model size.
Initialization timeMight have to wait for model to download before first use.Model is available immediately

Before you begin

  1. In your project-level build.gradle file, make sure to include Google's Maven repository in both your buildscript and allprojects sections.
  2. Add the dependencies for the ML Kit Android libraries to your module's app-level gradle file, which is usually app/build.gradle. Choose one of the following dependencies based on your needs:

    For bundling the model in your app:

        dependencies {
          // ...
          // Use this dependency to bundle the model with your app
          implementation 'com.google.mlkit:face-detection:16.0.2'
        }
        

    For using model within Google Play Service:

        dependencies {
          // ...
          // Use this dependency to use dynamically downloaded model in Google Play Service
          implementation 'com.google.android.gms:play-services-mlkit-face-detection:16.1.1'
        }
        
  3. If you choose to use the Google Play Service way: you can configure your app to automatically download the 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 ...>
            ...
          <meta-data
              android:name="com.google.mlkit.vision.DEPENDENCIES"
              android:value="face" />
          <!-- To use multiple models: android:value="face,model2,model3" -->
          </application>
        
    If you don't enable install-time model downloads, the model is downloaded the first time you run the scanner. Requests you make before the download has completed produce no results.

Input image guidelines

For face recognition, you should use an image with dimensions of at least 480x360 pixels. 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 detect 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, but keep 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 also impact accuracy. If you don't get acceptable results, ask 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 FaceDetectorOptions object. You can change the following settings:

Settings
setPerformanceMode FAST (default) | ACCURATE

Favor speed or accuracy when detecting faces.

setLandmarkMode NO_LANDMARKS (default) | ALL_LANDMARKS

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

setContourMode NO_CONTOURS (default) | ALL_CONTOURS

Whether to detect the contours of facial features. Contours are detected for only the most prominent face in an image.

setClassificationMode NO_CLASSIFICATIONS (default) | ALL_CLASSIFICATIONS

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

setMinFaceSize float (default: 0.1f)

Sets the smallest desired face size, expressed as the ratio of the width of the head to width of the image.

enableTracking false (default) | true

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

Note that when contour detection is enabled, only one face is detected, so face tracking doesn't produce useful results. For this reason, and to improve detection speed, don't enable both contour detection and face tracking.

For example:

Kotlin

// High-accuracy landmark detection and face classification
val highAccuracyOpts = FaceDetectorOptions.Builder()
        .setPerformanceMode(FaceDetectorOptions.PERFORMANCE_MODE_ACCURATE)
        .setLandmarkMode(FaceDetectorOptions.LANDMARK_MODE_ALL)
        .setClassificationMode(FaceDetectorOptions.CLASSIFICATION_MODE_ALL)
        .build()

// Real-time contour detection
val realTimeOpts = FaceDetectorOptions.Builder()
        .setContourMode(FaceDetectorOptions.CONTOUR_MODE_ALL)
        .build()

Java

// High-accuracy landmark detection and face classification
FaceDetectorOptions highAccuracyOpts =
        new FaceDetectorOptions.Builder()
                .setPerformanceMode(FaceDetectorOptions.PERFORMANCE_MODE_ACCURATE)
                .setLandmarkMode(FaceDetectorOptions.LANDMARK_MODE_ALL)
                .setClassificationMode(FaceDetectorOptions.CLASSIFICATION_MODE_ALL)
                .build();

// Real-time contour detection
FaceDetectorOptions realTimeOpts =
        new FaceDetectorOptions.Builder()
                .setContourMode(FaceDetectorOptions.CONTOUR_MODE_ALL)
                .build();

2. Prepare the input image

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

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

You can create an InputImage from different sources, each is explained below.

Using a media.Image

To create an InputImage object from a media.Image object, such as when you capture an image from a device's camera, pass the media.Image object and the image's rotation to InputImage.fromMediaImage().

If you use the CameraX library, the OnImageCapturedListener and ImageAnalysis.Analyzer classes calculate the rotation value for you.

Kotlin

private class YourImageAnalyzer : ImageAnalysis.Analyzer {

    override fun analyze(imageProxy: ImageProxy) {
        val mediaImage = imageProxy.image
        if (mediaImage != null) {
            val image = InputImage.fromMediaImage(mediaImage, imageProxy.imageInfo.rotationDegrees)
            // Pass image to an ML Kit Vision API
            // ...
        }
    }
}

Java

private class YourAnalyzer implements ImageAnalysis.Analyzer {

    @Override
    public void analyze(ImageProxy imageProxy) {
        Image mediaImage = imageProxy.getImage();
        if (mediaImage != null) {
          InputImage image =
                InputImage.fromMediaImage(mediaImage, imageProxy.getImageInfo().getRotationDegrees());
          // Pass image to an ML Kit Vision API
          // ...
        }
    }
}

If you don't use a camera library that gives you the image's rotation degree, you can calculate it from the device's rotation degree and the orientation of camera sensor in the device:

Kotlin

private val ORIENTATIONS = SparseIntArray()

init {
    ORIENTATIONS.append(Surface.ROTATION_0, 0)
    ORIENTATIONS.append(Surface.ROTATION_90, 90)
    ORIENTATIONS.append(Surface.ROTATION_180, 180)
    ORIENTATIONS.append(Surface.ROTATION_270, 270)
}

/**
 * Get the angle by which an image must be rotated given the device's current
 * orientation.
 */
@RequiresApi(api = Build.VERSION_CODES.LOLLIPOP)
@Throws(CameraAccessException::class)
private fun getRotationCompensation(cameraId: String, activity: Activity, isFrontFacing: Boolean): Int {
    // 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.
    val deviceRotation = activity.windowManager.defaultDisplay.rotation
    var rotationCompensation = ORIENTATIONS.get(deviceRotation)

    // Get the device's sensor orientation.
    val cameraManager = activity.getSystemService(CAMERA_SERVICE) as CameraManager
    val sensorOrientation = cameraManager
            .getCameraCharacteristics(cameraId)
            .get(CameraCharacteristics.SENSOR_ORIENTATION)!!

    if (isFrontFacing) {
        rotationCompensation = (sensorOrientation + rotationCompensation) % 360
    } else { // back-facing
        rotationCompensation = (sensorOrientation - rotationCompensation + 360) % 360
    }
    return rotationCompensation
}

Java

private static final SparseIntArray ORIENTATIONS = new SparseIntArray();
static {
    ORIENTATIONS.append(Surface.ROTATION_0, 0);
    ORIENTATIONS.append(Surface.ROTATION_90, 90);
    ORIENTATIONS.append(Surface.ROTATION_180, 180);
    ORIENTATIONS.append(Surface.ROTATION_270, 270);
}

/**
 * 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, boolean isFrontFacing)
        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);

    // Get the device's sensor orientation.
    CameraManager cameraManager = (CameraManager) activity.getSystemService(CAMERA_SERVICE);
    int sensorOrientation = cameraManager
            .getCameraCharacteristics(cameraId)
            .get(CameraCharacteristics.SENSOR_ORIENTATION);

    if (isFrontFacing) {
        rotationCompensation = (sensorOrientation + rotationCompensation) % 360;
    } else { // back-facing
        rotationCompensation = (sensorOrientation - rotationCompensation + 360) % 360;
    }
    return rotationCompensation;
}

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

Kotlin

val image = InputImage.fromMediaImage(mediaImage, rotation)

Java

InputImage image = InputImage.fromMediaImage(mediaImage, rotation);

Using a file URI

To create an InputImage object from a file URI, pass the app context and file URI to InputImage.fromFilePath(). This is useful when you use an ACTION_GET_CONTENT intent to prompt the user to select an image from their gallery app.

Kotlin

val image: InputImage
try {
    image = InputImage.fromFilePath(context, uri)
} catch (e: IOException) {
    e.printStackTrace()
}

Java

InputImage image;
try {
    image = InputImage.fromFilePath(context, uri);
} catch (IOException e) {
    e.printStackTrace();
}

Using a ByteBuffer or ByteArray

To create an InputImage object from a ByteBuffer or a ByteArray, first calculate the image rotation degree as previously described for media.Image input. Then, create the InputImage object with the buffer or array, together with image's height, width, color encoding format, and rotation degree:

Kotlin

val image = InputImage.fromByteBuffer(
        byteBuffer,
        /* image width */ 480,
        /* image height */ 360,
        rotationDegrees,
        InputImage.IMAGE_FORMAT_NV21 // or IMAGE_FORMAT_YV12
)
// Or:
val image = InputImage.fromByteArray(
        byteArray,
        /* image width */ 480,
        /* image height */ 360,
        rotationDegrees,
        InputImage.IMAGE_FORMAT_NV21 // or IMAGE_FORMAT_YV12
)

Java

InputImage image = InputImage.fromByteBuffer(byteBuffer,
        /* image width */ 480,
        /* image height */ 360,
        rotationDegrees,
        InputImage.IMAGE_FORMAT_NV21 // or IMAGE_FORMAT_YV12
);
// Or:
InputImage image = InputImage.fromByteArray(
        byteArray,
        /* image width */480,
        /* image height */360,
        rotation,
        InputImage.IMAGE_FORMAT_NV21 // or IMAGE_FORMAT_YV12
);

Using a Bitmap

To create an InputImage object from a Bitmap object, make the following declaration:

Kotlin

val image = InputImage.fromBitmap(bitmap, 0)

Java

InputImage image = InputImage.fromBitmap(bitmap, rotationDegree);

The image is represented by a Bitmap object together with rotation degrees.

3. Get an instance of FaceDetector

Kotlin

val detector = FaceDetection.getClient(options)
// Or, to use the default option:
// val detector = FaceDetection.getClient();

Java

FaceDetector detector = FaceDetection.getClient(options);
// Or use the default options:
// FaceDetector detector = FaceDetection.getClient();

4. Process the image

Pass the image to the process method:

Kotlin

val result = detector.process(image)
        .addOnSuccessListener { faces ->
            // Task completed successfully
            // ...
        }
        .addOnFailureListener { e ->
            // Task failed with an exception
            // ...
        }

Java

Task<List<Face>> result =
        detector.process(image)
                .addOnSuccessListener(
                        new OnSuccessListener<List<Face>>() {
                            @Override
                            public void onSuccess(List<Face> faces) {
                                // Task completed successfully
                                // ...
                            }
                        })
                .addOnFailureListener(
                        new OnFailureListener() {
                            @Override
                            public void onFailure(@NonNull Exception e) {
                                // Task failed with an exception
                                // ...
                            }
                        });

5. Get information about detected faces

If the face detection operation succeeds, a list of Face objects are passed to the success listener. Each Face 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:

Kotlin

for (face in faces) {
    val bounds = face.boundingBox
    val rotY = face.headEulerAngleY // Head is rotated to the right rotY degrees
    val rotZ = face.headEulerAngleZ // Head is tilted sideways rotZ degrees

    // If landmark detection was enabled (mouth, ears, eyes, cheeks, and
    // nose available):
    val leftEar = face.getLandmark(FaceLandmark.LEFT_EAR)
    leftEar?.let {
        val leftEarPos = leftEar.position
    }

    // If contour detection was enabled:
    val leftEyeContour = face.getContour(FaceContour.LEFT_EYE)?.points
    val upperLipBottomContour = face.getContour(FaceContour.UPPER_LIP_BOTTOM)?.points

    // If classification was enabled:
    if (face.smilingProbability != null) {
        val smileProb = face.smilingProbability
    }
    if (face.rightEyeOpenProbability != null) {
        val rightEyeOpenProb = face.rightEyeOpenProbability
    }

    // If face tracking was enabled:
    if (face.trackingId != null) {
        val id = face.trackingId
    }
}

Java

for (Face 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):
    FaceLandmark leftEar = face.getLandmark(FaceLandmark.LEFT_EAR);
    if (leftEar != null) {
        PointF leftEarPos = leftEar.getPosition();
    }

    // If contour detection was enabled:
    List<PointF> leftEyeContour =
            face.getContour(FaceContour.LEFT_EYE).getPoints();
    List<PointF> upperLipBottomContour =
            face.getContour(FaceContour.UPPER_LIP_BOTTOM).getPoints();

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

    // If face tracking was enabled:
    if (face.getTrackingId() != null) {
        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 Face Detection Concepts for details about how contours are represented.

The following image illustrates how these points map to a face, click the image to enlarge it:

example detected face contour mesh

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
    Classification
    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.

  • If you use the Camera or camera2 API, throttle calls to the detector. If a new video frame becomes available while the detector is running, drop the frame. See the VisionProcessorBase class in the quickstart sample app for an example.
  • If you use the CameraX API, be sure that backpressure strategy is set to its default value ImageAnalysis.STRATEGY_KEEP_ONLY_LATEST. This guarantees only one image will be delivered for analysis at a time. If more images are produced when the analyzer is busy, they will be dropped automatically and not queued for delivery. Once the image being analyzed is closed by calling ImageProxy.close(), the next latest image will be delivered.
  • If you use 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. This renders 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.
  • Next steps

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