You can use ML Kit to detect faces in images and video.
Before you begin
- If you haven't already, add Firebase to your Android project .
- 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 ' }
- 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.xmlfile:< application ... > ... < meta - data android : name = "com.google.firebase.ml.vision.DEPENDENCIES" android : value = "face" / > < ! -- To use multiple models : android : value = "face,model2,model3" -- > < / application >
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 aFirebaseVisionFaceDetectorOptions
object.
You can change the following settings: FAST
(default)
| ACCURATE
Favor speed or accuracy when detecting faces.
NO_LANDMARKS
(default)
| ALL_LANDMARKS
Whether to attempt to identify facial "landmarks": eyes, ears, nose, cheeks, mouth, and so on.
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.
NO_CLASSIFICATIONS
(default)
| ALL_CLASSIFICATIONS
Whether or not to classify faces into categories such as "smiling", and "eyes open".
float
(default: 0.1f
) The minimum size, relative to the image, of faces to detect.
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:
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 aFirebaseVisionImage
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 480x360pixels. If you are recognizing faces in real time, capturing frames at this minimum resolution can help reduce latency.
-
Create a
FirebaseVisionImageobject from your image.-
To create a
FirebaseVisionImageobject from amedia.Imageobject, such as when capturing an image from a device's camera, pass themedia.Imageobject and the image's rotation toFirebaseVisionImage.fromMediaImage().If you use the CameraX library, the
OnImageCapturedListenerandImageAnalysis.Analyzerclasses 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.Imageobject and the rotation value toFirebaseVisionImage.fromMediaImage():Java
FirebaseVisionImage image = FirebaseVisionImage . fromMediaImage ( mediaImage , rotation );
Kotlin
val image = FirebaseVisionImage . fromMediaImage ( mediaImage , rotation )
- To create a
FirebaseVisionImageobject from a file URI, pass the app context and file URI toFirebaseVisionImage.fromFilePath(). This is useful when you use anACTION_GET_CONTENTintent 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
FirebaseVisionImageobject from aByteBufferor a byte array, first calculate the image rotation as described above formedia.Imageinput.Then, create a
FirebaseVisionImageMetadataobject 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 a
FirebaseVisionImageobject:Java
FirebaseVisionImage image = FirebaseVisionImage . fromByteBuffer ( buffer , metadata ); // Or: FirebaseVisionImage image = FirebaseVisionImage.fromByteArray(byteArray, metadata);
-
