Detect Faces with ML Kit on Android (original) (raw)

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

Before you begin

1. If you haven't already,add Firebase to your Android project.
2. Add the dependencies for the ML Kit Android libraries to your module (app-level) Gradle file (usually app/build.gradle):
   apply plugin: 'com.android.application'
   apply plugin: 'com.google.gms.google-services'
   dependencies {
   // ...

implementation 'com.google.firebase:firebase-ml-vision:24.0.3'
// If you want to detect face contours (landmark detection and classification
// don't require this additional model):
implementation 'com.google.firebase:firebase-ml-vision-face-model:20.0.1'
} 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'sAndroidManifest.xml file:
<application ...>
...



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 seeTips 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. SeeFace 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 aFirebaseVisionFaceDetectorOptions object. You can change the following settings:

For example:

Java

// High-accuracy landmark detection and face classification FirebaseVisionFaceDetectorOptions highAccuracyOpts = new FirebaseVisionFaceDetectorOptions.Builder() .setPerformanceMode(FirebaseVisionFaceDetectorOptions.ACCURATE) .setLandmarkMode(FirebaseVisionFaceDetectorOptions.ALL_LANDMARKS) .setClassificationMode(FirebaseVisionFaceDetectorOptions.ALL_CLASSIFICATIONS) .build();

// Real-time contour detection of multiple faces FirebaseVisionFaceDetectorOptions realTimeOpts = new FirebaseVisionFaceDetectorOptions.Builder() .setContourMode(FirebaseVisionFaceDetectorOptions.ALL_CONTOURS) .build();

Kotlin

// High-accuracy landmark detection and face classification val highAccuracyOpts = FirebaseVisionFaceDetectorOptions.Builder() .setPerformanceMode(FirebaseVisionFaceDetectorOptions.ACCURATE) .setLandmarkMode(FirebaseVisionFaceDetectorOptions.ALL_LANDMARKS) .setClassificationMode(FirebaseVisionFaceDetectorOptions.ALL_CLASSIFICATIONS) .build()

// Real-time contour detection of multiple faces val realTimeOpts = FirebaseVisionFaceDetectorOptions.Builder() .setContourMode(FirebaseVisionFaceDetectorOptions.ALL_CONTOURS) .build()

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 theFirebaseVisionFaceDetector's detectInImage method.

For face recognition, you should use an image with dimensions of at least480x360 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 amedia.Image object, such as when capturing an image from a device's camera, pass the media.Image object and the image's rotation to FirebaseVisionImage.fromMediaImage().
     If you use the CameraX library, the OnImageCapturedListener andImageAnalysis.Analyzer classes calculate the rotation value for you, so you just need to convert the rotation to one of ML Kit'sROTATION_ constants before callingFirebaseVisionImage.fromMediaImage():

   Java

   private class YourAnalyzer implements ImageAnalysis.Analyzer {
   private int degreesToFirebaseRotation(int degrees) {
   switch (degrees) {
   case 0:
   return FirebaseVisionImageMetadata.ROTATION_0;
   case 90:
   return FirebaseVisionImageMetadata.ROTATION_90;
   case 180:
   return FirebaseVisionImageMetadata.ROTATION_180;
   case 270:
   return FirebaseVisionImageMetadata.ROTATION_270;
   default:
   throw new IllegalArgumentException(
   "Rotation must be 0, 90, 180, or 270.");
   }
   }
   @Override
   public void analyze(ImageProxy imageProxy, int degrees) {
   if (imageProxy == null || imageProxy.getImage() == null) {
   return;
   }
   Image mediaImage = imageProxy.getImage();
   int rotation = degreesToFirebaseRotation(degrees);
   FirebaseVisionImage image =
   FirebaseVisionImage.fromMediaImage(mediaImage, rotation);
   // Pass image to an ML Kit Vision API
   // ...
   }
   }

   Kotlin

   private class YourImageAnalyzer : ImageAnalysis.Analyzer {
   private fun degreesToFirebaseRotation(degrees: Int): Int = when(degrees) {
   0 -> FirebaseVisionImageMetadata.ROTATION_0
   90 -> FirebaseVisionImageMetadata.ROTATION_90
   180 -> FirebaseVisionImageMetadata.ROTATION_180
   270 -> FirebaseVisionImageMetadata.ROTATION_270
   else -> throw Exception("Rotation must be 0, 90, 180, or 270.")
   }
   override fun analyze(imageProxy: ImageProxy?, degrees: Int) {
   val mediaImage = imageProxy?.image
   val imageRotation = degreesToFirebaseRotation(degrees)
   if (mediaImage != null) {
   val image = FirebaseVisionImage.fromMediaImage(mediaImage, imageRotation)
   // Pass image to an ML Kit Vision API
   // ...
   }
   }
   }
   If you don't use a camera library that gives you the image's rotation, you can calculate it from the device's rotation and the orientation of camera sensor in the device:

   Java

   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
     .getCameraCharacteristics(cameraId)
     .get(CameraCharacteristics.SENSOR_ORIENTATION);
     rotationCompensation = (rotationCompensation + sensorOrientation + 270) % 360;
     // Return the corresponding FirebaseVisionImageMetadata rotation value.
     int result;
     switch (rotationCompensation) {
     case 0:
     result = FirebaseVisionImageMetadata.ROTATION_0;
     break;
     case 90:
     result = FirebaseVisionImageMetadata.ROTATION_90;
     break;
     case 180:
     result = FirebaseVisionImageMetadata.ROTATION_180;
     break;
     case 270:
     result = FirebaseVisionImageMetadata.ROTATION_270;
     break;
     default:
     result = FirebaseVisionImageMetadata.ROTATION_0;
     Log.e(TAG, "Bad rotation value: " + rotationCompensation);
     }
     return result;
     }

   Kotlin

   private val ORIENTATIONS = SparseIntArray()
   init {
   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)
     @Throws(CameraAccessException::class)
     private fun getRotationCompensation(cameraId: String, activity: Activity, context: Context): 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)
     // 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.
     val cameraManager = context.getSystemService(CAMERA_SERVICE) as CameraManager
     val sensorOrientation = cameraManager
     .getCameraCharacteristics(cameraId)
     .get(CameraCharacteristics.SENSOR_ORIENTATION)!!
     rotationCompensation = (rotationCompensation + sensorOrientation + 270) % 360
     // Return the corresponding FirebaseVisionImageMetadata rotation value.
     val result: Int
     when (rotationCompensation) {
     0 -> result = FirebaseVisionImageMetadata.ROTATION_0
     90 -> result = FirebaseVisionImageMetadata.ROTATION_90
     180 -> result = FirebaseVisionImageMetadata.ROTATION_180
     270 -> result = FirebaseVisionImageMetadata.ROTATION_270
     else -> {
     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():

   Java

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

   Kotlin

   val image = FirebaseVisionImage.fromMediaImage(mediaImage, rotation)

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

   Java

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

   Kotlin

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

   • To create a FirebaseVisionImage object from aByteBuffer or a byte array, first calculate the image rotation as described above for media.Image input.
     Then, create a FirebaseVisionImageMetadata object that contains the image's height, width, color encoding format, and rotation:

   Java

   FirebaseVisionImageMetadata metadata = new FirebaseVisionImageMetadata.Builder()
   .setWidth(480) // 480x360 is typically sufficient for
   .setHeight(360) // image recognition
   .setFormat(FirebaseVisionImageMetadata.IMAGE_FORMAT_NV21)
   .setRotation(rotation)
   .build();

   Kotlin

   val metadata = FirebaseVisionImageMetadata.Builder()
   .setWidth(480) // 480x360 is typically sufficient for
   .setHeight(360) // image recognition
   .setFormat(FirebaseVisionImageMetadata.IMAGE_FORMAT_NV21)
   .setRotation(rotation)
   .build()
   Use the buffer or array, and the metadata object, to create aFirebaseVisionImage object:

   Java

   FirebaseVisionImage image = FirebaseVisionImage.fromByteBuffer(buffer, metadata);
   // Or: FirebaseVisionImage image = FirebaseVisionImage.fromByteArray(byteArray, metadata);

   Kotlin

   val image = FirebaseVisionImage.fromByteBuffer(buffer, metadata)
   // Or: val image = FirebaseVisionImage.fromByteArray(byteArray, metadata)

   • To create a FirebaseVisionImage object from aBitmap object:

   Java

   FirebaseVisionImage image = FirebaseVisionImage.fromBitmap(bitmap);

   Kotlin

   val image = FirebaseVisionImage.fromBitmap(bitmap)
   The image represented by the Bitmap object must be upright, with no additional rotation required.

2. Get an instance of FirebaseVisionFaceDetector:

Java

FirebaseVisionFaceDetector detector = FirebaseVision.getInstance()
.getVisionFaceDetector(options);

Kotlin

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

Java

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

Kotlin

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

3. Get information about detected faces

If the face recognition operation succeeds, a list ofFirebaseVisionFace 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:

Java

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 =
        face.getContour(FirebaseVisionFaceContour.LEFT_EYE).getPoints();
List<FirebaseVisionPoint> upperLipBottomContour =
        face.getContour(FirebaseVisionFaceContour.UPPER_LIP_BOTTOM).getPoints();

// 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();
}

}

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(FirebaseVisionFaceLandmark.LEFT_EAR)
leftEar?.let {
    val leftEarPos = leftEar.position
}

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

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

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

}

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
  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.
• 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.
• If you use the Camera2 API, capture images inImageFormat.YUV_420_888 format.
  If you use the older Camera API, capture images inImageFormat.NV21 format.