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.xml
file:
<application ...>
...
<meta-data
android:name="com.google.firebase.ml.vision.DEPENDENCIES"
android:value="face" />
<!-- To use multiple models: android:value="face,model2,model3" -->
</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.
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
.
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:
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 are
detected for only the most prominent face in an image.
|
Classify faces
|
NO_CLASSIFICATIONS
(default)
|
ALL_CLASSIFICATIONS
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.
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+KTX
// 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 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.
Create a
FirebaseVisionImage
object from your
image.
-
To create a
FirebaseVisionImage
object from a
media.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
and
ImageAnalysis.Analyzer
classes calculate the rotation value
for you, so you just need to convert the rotation to one of ML Kit's
ROTATION_
constants before calling
FirebaseVisionImage.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+KTX
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+KTX
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+KTX
val image = FirebaseVisionImage.fromMediaImage(mediaImage, rotation)
- To create a
FirebaseVisionImage
object from a file URI, pass
the app context and file URI to
FirebaseVisionImage.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+KTX
val image: FirebaseVisionImage
try {
image = FirebaseVisionImage.fromFilePath(context, uri)
} catch (e: IOException) {
e.printStackTrace()
}
- To create a
FirebaseVisionImage
object from a
ByteBuffer
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+KTX
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
FirebaseVisionImage
object:
Java
FirebaseVisionImage image = FirebaseVisionImage.fromByteBuffer(buffer, metadata);
// Or: FirebaseVisionImage image = FirebaseVisionImage.fromByteArray(byteArray, metadata);
Kotlin+KTX
val image = FirebaseVisionImage.fromByteBuffer(buffer, metadata)
// Or: val image = FirebaseVisionImage.fromByteArray(byteArray, metadata)
- To create a
FirebaseVisionImage
object from a
Bitmap
object:
Java
FirebaseVisionImage image = FirebaseVisionImage.fromBitmap(bitmap);
Kotlin+KTX
val image = FirebaseVisionImage.fromBitmap(bitmap)
The image represented by the
Bitmap
object must
be upright, with no additional rotation required.
Get an instance of
FirebaseVisionFaceDetector
:
Java
FirebaseVisionFaceDetector detector = FirebaseVision.getInstance()
.getVisionFaceDetector(options);
Kotlin+KTX
val detector = FirebaseVision.getInstance()
.getVisionFaceDetector(options)
Finally, pass the image to the
detectInImage
method:
Java
Task<List<FirebaseVisionFace>> result =
detector.detectInImage(image)
.addOnSuccessListener(
new OnSuccessListener<List<FirebaseVisionFace>>() {
@Override
public void onSuccess(List<FirebaseVisionFace> faces) {
// Task completed successfully
// ...
}
})
.addOnFailureListener(
new OnFailureListener() {
@Override
public void onFailure(@NonNull Exception e) {
// Task failed with an exception
// ...
}
});
Kotlin+KTX
val result = detector.detectInImage(image)
.addOnSuccessListener { faces ->
// Task completed successfully
// ...
}
.addOnFailureListener { e ->
// Task failed with an exception
// ...
}
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:
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+KTX
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):