FaceNet Android supports two face detection backends: Google MLKit and MediaPipe Face Detector. Both detect faces in images and provide bounding boxes for cropping.
Available detectors
MLKit Face Detector
MLKit provides Google’s on-device face detection API with two performance modes:
- Fast mode - Used for real-time camera frame detection
- Accurate mode - Used for processing user-selected images
The MLKit detector automatically switches between modes based on the operation.
MediaPipe uses the BlazeFace short-range model for face detection. It runs in IMAGE mode and processes both camera frames and static images with the same configuration.
Switching detectors
To change the face detection backend, modify AppModule.kt:15:
@Module
@ComponentScan("com.ml.shubham0204.facenet_android")
class AppModule {
// Set to true for MLKit, false for MediaPipe
private var isMLKit = true
@Single
fun provideFaceDetector(context: Context): BaseFaceDetector = if (isMLKit) {
MLKitFaceDetector(context)
} else {
MediapipeFaceDetector(context)
}
}
MLKit is enabled by default and provides better performance on most devices through its dual-mode approach.
MLKit configuration
The MLKit detector creates two detector instances in MLKitFaceDetector.kt:20-28:
// For real-time camera detection
private val realTimeOpts = FaceDetectorOptions.Builder()
.setPerformanceMode(FaceDetectorOptions.PERFORMANCE_MODE_FAST)
.build()
private val realTimeFaceDetector = FaceDetection.getClient(realTimeOpts)
// For static image processing
private val highAccuracyOpts = FaceDetectorOptions.Builder()
.setPerformanceMode(FaceDetectorOptions.PERFORMANCE_MODE_ACCURATE)
.build()
private val highAccuracyFaceDetector = FaceDetection.getClient(highAccuracyOpts)
Customizing MLKit options
You can configure additional MLKit options:
val options = FaceDetectorOptions.Builder()
.setPerformanceMode(FaceDetectorOptions.PERFORMANCE_MODE_ACCURATE)
.setLandmarkMode(FaceDetectorOptions.LANDMARK_MODE_ALL)
.setClassificationMode(FaceDetectorOptions.CLASSIFICATION_MODE_ALL)
.setMinFaceSize(0.15f) // Minimum face size relative to image
.enableTracking() // Track faces across frames
.build()
Enabling additional features like landmarks or classification increases processing time and is not required for face recognition.
MediapipeFaceDetector.kt:23-31:
private val modelName = "blaze_face_short_range.tflite"
private val baseOptions = BaseOptions.builder()
.setModelAssetPath(modelName)
.build()
private val faceDetectorOptions = FaceDetector.FaceDetectorOptions
.builder()
.setBaseOptions(baseOptions)
.setRunningMode(RunningMode.IMAGE)
.build()
private val faceDetector = FaceDetector.createFromOptions(context, faceDetectorOptions)
val options = FaceDetector.FaceDetectorOptions
.builder()
.setBaseOptions(baseOptions)
.setRunningMode(RunningMode.IMAGE)
.setMinDetectionConfidence(0.5f) // Detection threshold
.setMinSuppressionThreshold(0.3f) // NMS threshold
.build()
| Detector | Camera Frame Detection | Static Image Detection | Model Size |
|---|
| MLKit Fast | ~15-25ms | N/A | Bundled with Google Play Services |
| MLKit Accurate | N/A | ~30-50ms | Bundled with Google Play Services |
| MediaPipe | ~20-35ms | ~20-35ms | 2.8 MB |
MLKit relies on Google Play Services and may download models on first use. MediaPipe bundles the model in your APK, increasing app size but ensuring offline availability.
When to use each detector
Use MLKit when:
- You need the fastest real-time detection
- Your app targets devices with Google Play Services
- You want to minimize APK size
Use MediaPipe when:
- You need guaranteed offline functionality
- You’re targeting devices without Google Play Services
- You want consistent performance across modes