Android FaceDetector.Face Euler angles are 0 all the time

Android FaceDetector.Face Euler Angles are 0 All the Time

Problem Description

The Android FaceDetector.Face class provides Euler angles (roll, pitch, yaw) to describe the orientation of a detected face. However, these angles often return 0 for all axes, even when the face is clearly tilted or rotated in the image.

Causes

• Limited Functionality: The Android FaceDetector API is relatively basic and may not be designed to accurately determine face orientation in all cases.
• Performance Optimization: The API might prioritize speed over accuracy, and simplifying calculations for Euler angles could be a way to achieve this.
• Image Quality: Poor lighting, blurriness, or occlusions in the image can hinder accurate face orientation detection.

Workarounds

Alternative Libraries

Consider using more sophisticated face detection and pose estimation libraries like:

• Firebase ML Kit: Provides advanced face detection with features like head pose estimation.
• OpenCV: A popular computer vision library with robust face detection and pose estimation algorithms.

Manual Calculation

If you need to estimate face orientation manually, you can use the following approaches:

• Landmark Positions: Analyze the relative positions of detected face landmarks (e.g., eyes, nose, mouth) to determine tilt and rotation.
• Perspective Transformation: Apply geometric transformations to the image to correct for perspective distortions and estimate face orientation.

Example Code

Android FaceDetector

import android.graphics.Bitmap;
import android.graphics.Canvas;
import android.graphics.Paint;
import android.media.FaceDetector;

// ...

Bitmap bitmap = // Your image
FaceDetector faceDetector = new FaceDetector.Builder(this).setTrackingEnabled(false).build();
FaceDetector.Faces faces = faceDetector.findFaces(bitmap);

for (Face face : faces) {
  float roll = face.eulerY; // Often 0
  float pitch = face.eulerX; // Often 0
  float yaw = face.eulerZ; // Often 0

  // ... use angles for further processing
}

Firebase ML Kit

import com.google.firebase.ml.vision.FirebaseVision;
import com.google.firebase.ml.vision.common.FirebaseVisionImage;
import com.google.firebase.ml.vision.face.FirebaseVisionFace;
import com.google.firebase.ml.vision.face.FirebaseVisionFaceDetector;
import com.google.firebase.ml.vision.face.FirebaseVisionFaceDetectorOptions;

// ...

FirebaseVisionFaceDetectorOptions options = new FirebaseVisionFaceDetectorOptions.Builder()
    .setPerformanceMode(FirebaseVisionFaceDetectorOptions.PERFORMANCE_MODE_ACCURATE)
    .setLandmarkMode(FirebaseVisionFaceDetectorOptions.LANDMARK_MODE_ALL)
    .setClassificationMode(FirebaseVisionFaceDetectorOptions.CLASSIFICATION_MODE_ALL)
    .build();

FirebaseVisionImage image = FirebaseVisionImage.fromBitmap(bitmap);
FirebaseVisionFaceDetector detector = FirebaseVision.getInstance().getVisionFaceDetector(options);

detector.detectInImage(image)
    .addOnSuccessListener(faces -> {
        for (FirebaseVisionFace face : faces) {
            float roll = face.getHeadEulerAngleY(); // Accurate roll angle
            float pitch = face.getHeadEulerAngleX(); // Accurate pitch angle
            float yaw = face.getHeadEulerAngleZ(); // Accurate yaw angle

            // ... use angles for further processing
        }
    });

Conclusion

While the Android FaceDetector.Face Euler angles may not be reliable, alternative libraries and manual calculations provide better options for estimating face orientation. Consider your project’s requirements and select the approach that best suits your needs.