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

<br />

This API is available using either an unbundled library that must be downloaded before use or a bundled library that increases your app size. See [this guide](https://developers.google.com/ml-kit/tips/installation-paths.md.txt) for more information on the differences between the two installation options.

| Feature | Unbundled | Bundled |
|---|---|---|
| Implementation | Model is dynamically downloaded via Google Play Services. | Model is statically linked to your app at build time. |
| App size | About 800 KB size increase. | About 6.9 MB size increase. |
| Initialization time | Might have to wait for model to download before first use. | Model is available immediately |

## Try it out

- Play around with [the sample app](https://github.com/googlesamples/mlkit/tree/master/android/vision-quickstart) to see an example usage of this API.
- Try the code yourself with the [codelab](https://codelabs.developers.google.com/codelabs/mlkit-android).

## Before you begin

> [!NOTE]
> This API requires Android API level 23 or above. Make sure that your app's build file uses a `minSdkVersion` value of 23 or higher.

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 with your app:**

       dependencies {
         // ...
         // Use this dependency to bundle the model with your app
         implementation 'com.google.mlkit:face-detection:16.1.7'
       }

   **For using the model in Google Play Services:**

       dependencies {
         // ...
         // Use this dependency to use the dynamically downloaded model in Google Play Services
         implementation 'com.google.android.gms:play-services-mlkit-face-detection:17.1.0'
       }

3. **If you choose to use the model in Google Play Services** , 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>

   You can also explicitly check the model availability and request download through
   Google Play services [ModuleInstallClient API](https://developers.google.com/android/guides/module-install-apis.md.txt).

   If you don't enable install-time model downloads or request explicit download,
   the model is downloaded the first time you run the detector. 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](https://developers.google.com/ml-kit/vision/face-detection/android.md.txt#performance_tips).

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](https://developers.google.com/ml-kit/vision/face-detection/face-detection-concepts.md.txt#landmarks).

## 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`](https://developers.google.com/android/reference/com/google/mlkit/vision/face/FaceDetectorOptions.Builder.md.txt) object. You can change the following settings:

<br />

| Settings ||
|---|---|
| [`setPerformanceMode`](https://developers.google.com/android/reference/com/google/mlkit/vision/face/FaceDetectorOptions.Builder.md.txt#setPerformanceMode(int)) | [`PERFORMANCE_MODE_FAST`](https://developers.google.com/android/reference/com/google/mlkit/vision/face/FaceDetectorOptions.md.txt#PERFORMANCE_MODE_FAST) (default) \| [`PERFORMANCE_MODE_ACCURATE`](https://developers.google.com/android/reference/com/google/mlkit/vision/face/FaceDetectorOptions.md.txt#PERFORMANCE_MODE_ACCURATE) Favor speed or accuracy when detecting faces. |
| [`setLandmarkMode`](https://developers.google.com/android/reference/com/google/mlkit/vision/face/FaceDetectorOptions.Builder.md.txt#setLandmarkMode(int)) | [`LANDMARK_MODE_NONE`](https://developers.google.com/android/reference/com/google/mlkit/vision/face/FaceDetectorOptions.md.txt#LANDMARK_MODE_NONE) (default) \| [`LANDMARK_MODE_ALL`](https://developers.google.com/android/reference/com/google/mlkit/vision/face/FaceDetectorOptions.md.txt#LANDMARK_MODE_ALL) Whether to attempt to identify facial "landmarks": eyes, ears, nose, cheeks, mouth, and so on. |
| [`setContourMode`](https://developers.google.com/android/reference/com/google/mlkit/vision/face/FaceDetectorOptions.Builder.md.txt#setContourMode(int)) | [`CONTOUR_MODE_NONE`](https://developers.google.com/android/reference/com/google/mlkit/vision/face/FaceDetectorOptions.md.txt#CONTOUR_MODE_NONE) (default) \| [`CONTOUR_MODE_ALL`](https://developers.google.com/android/reference/com/google/mlkit/vision/face/FaceDetectorOptions.md.txt#CONTOUR_MODE_ALL) Whether to detect the contours of facial features. Contours are detected for only the most prominent face in an image. |
| [`setClassificationMode`](https://developers.google.com/android/reference/com/google/mlkit/vision/face/FaceDetectorOptions.Builder.md.txt#setClassificationMode(int)) | [`CLASSIFICATION_MODE_NONE`](https://developers.google.com/android/reference/com/google/mlkit/vision/face/FaceDetectorOptions.md.txt#CLASSIFICATION_MODE_NONE) (default) \| [`CLASSIFICATION_MODE_ALL`](https://developers.google.com/android/reference/com/google/mlkit/vision/face/FaceDetectorOptions.md.txt#CLASSIFICATION_MODE_ALL) Whether or not to classify faces into categories such as "smiling", and "eyes open". |
| [`setMinFaceSize`](https://developers.google.com/android/reference/com/google/mlkit/vision/face/FaceDetectorOptions.Builder.md.txt#setMinFaceSize(float)) | `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`](https://developers.google.com/android/reference/com/google/mlkit/vision/face/FaceDetectorOptions.Builder.md.txt#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

```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

```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.

<br />

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`](https://developers.google.com/android/reference/com/google/mlkit/vision/common/InputImage.md.txt)
object from different sources, each is explained below.

### Using a `media.Image`


To create an [`InputImage`](https://developers.google.com/android/reference/com/google/mlkit/vision/common/InputImage.md.txt)
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](https://developer.android.com/training/camerax) library, the `OnImageCapturedListener` and
`ImageAnalysis.Analyzer` classes calculate the rotation value
for you.

### Kotlin

```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

```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

```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

```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

```kotlin
val image = InputImage.fromMediaImage(mediaImage, rotation)
```

### Java

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

### Using a file URI

To create an [`InputImage`](https://developers.google.com/android/reference/com/google/mlkit/vision/common/InputImage.md.txt)
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

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

### Java

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

### Using a `ByteBuffer` or `ByteArray`

To create an [`InputImage`](https://developers.google.com/android/reference/com/google/mlkit/vision/common/InputImage.md.txt)
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

```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

```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`](https://developers.google.com/android/reference/com/google/mlkit/vision/common/InputImage.md.txt)
object from a `Bitmap` object, make the following declaration:

### Kotlin

```kotlin
val image = InputImage.fromBitmap(bitmap, 0)
```

### Java

```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

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

### Java

```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:

<br />

### Kotlin

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

### Java

```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
                                // ...
                            }
                        });
```
Note: If you are using the [`CameraX`](https://developer.android.com/jetpack/androidx/releases/camera) API, make sure to close the `ImageProxy` when finish using it, e.g., by adding an `OnCompleteListener` to the `Task` returned from the `process` method. See the [`VisionProcessorBase`](https://github.com/googlesamples/mlkit/blob/e949654bef09e97b237c7692349c52ee06e9fd9e/android/vision-quickstart/app/src/main/java/com/google/mlkit/vision/demo/java/VisionProcessorBase.java#L178-L181) class in the quickstart sample app for an example.

## 5. Get information about detected faces

If the face detection operation succeeds, a list of [`Face`](https://developers.google.com/android/reference/com/google/mlkit/vision/face/Face.md.txt) 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:

<br />

### Kotlin

```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

```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](https://developers.google.com/ml-kit/vision/face-detection/face-detection-concepts#contours) 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](https://developers.google.com/static/ml-kit/vision/face-detection/images/face_contours.svg)](https://developers.google.com/static/ml-kit/vision/face-detection/images/face_contours.svg)

## 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`](https://developer.android.com/reference/android/hardware/Camera) or [`camera2`](https://developer.android.com/reference/android/hardware/camera2/package-summary) API, throttle calls to the detector. If a new video frame becomes available while the detector is running, drop the frame. See the [`VisionProcessorBase`](https://github.com/googlesamples/mlkit/blob/master/android/vision-quickstart/app/src/main/java/com/google/mlkit/vision/demo/kotlin/VisionProcessorBase.kt) class in the quickstart sample app for an example.
- If you use the [`CameraX`](https://developer.android.com/jetpack/androidx/releases/camera) API, be sure that backpressure strategy is set to its default value [`ImageAnalysis.STRATEGY_KEEP_ONLY_LATEST`](https://developer.android.com/reference/androidx/camera/core/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`](https://github.com/googlesamples/mlkit/blob/master/android/vision-quickstart/app/src/main/java/com/google/mlkit/vision/demo/CameraSourcePreview.java) and [`GraphicOverlay`](https://github.com/googlesamples/mlkit/blob/master/android/vision-quickstart/app/src/main/java/com/google/mlkit/vision/demo/GraphicOverlay.java) 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.