face_detection_tflite 1.0.0

Face & landmark detection using on-device TFLite models.

face_detection_tflite

A Dart/Flutter package that runs an on-device face and landmark detection with TensorFlow Lite:

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Inspiration

This package is a Flutter/Dart port inspired by and adapted from the original Python project patlevin/face-detection-tflite. Many thanks to the original author.

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Table of Contents

• Features
• Quick Start
• Models
• Types
• Example

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Features

• On-device face detection (multiple SSD variants)
• 468-point face mesh, face landmarks, iris landmarks and bounding boxes
• All coordinates returned in pre-normalized pixel space (Point<double>)
• Truly cross-platform: compatible with Android, iOS, macOS, Windows, and Linux
• The example/ app illustrates how to detect and render results on images: bounding boxes, a 468-point face mesh, and iris landmarks.

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Quick Start

import 'dart:typed_data';
import 'package:face_detection_tflite/face_detection_tflite.dart';

final detector = FaceDetector();

// Initialize once; choose the model variant you prefer
Future<void> init() async {
  await detector.initialize(model: FaceDetectionModel.backCamera);
}

// Full pipeline
Future<void> analyze(Uint8List imageBytes) async {
  final result = await detector.detectFaces(imageBytes);

  for (final face in result.faces) {
    final bbox    = face.bboxCorners;     // List<Point<double>> (4 pixel corners)
    final mesh    = face.mesh;            // List<Point<double>> (468-point mesh in pixels)
    final irises  = face.irises;          // List<Point<double>> (iris landmarks in pixels)
    final lm      = face.landmarks;       // Map<FaceIndex, Point<double>> (keypoints in pixels)

    // Iterate all landmarks with their FaceIndex keys
    for (final entry in lm.entries) {
      final idx = entry.key;              // FaceIndex.leftEye, rightEye, noseTip, etc.
      final pt  = entry.value;            // Point<double> in pixels
      final px  = pt.x;
      final py  = pt.y;
    }

    // Access landmarks directly by enum
    final leftEye  = face.landmarks[FaceIndex.leftEye];    // Point<double> in pixels
    final rightEye = face.landmarks[FaceIndex.rightEye];   // Point<double> in pixels
  }
}

// When you're done with it
detector.dispose();

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Models

You can choose from several detection models depending on your use case:

• FaceDetectionModel.backCamera – larger model for group shots or images with smaller faces (default).
• FaceDetectionModel.frontCamera – optimized for selfies and close-up portraits.
• FaceDetectionModel.short – best for short-range images (faces within ~2m).
• FaceDetectionModel.full – best for mid-range images (faces within ~5m).
• FaceDetectionModel.fullSparse – same detection quality as full but runs up to 30% faster on CPU (slightly higher precision, slightly lower recall).

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Types

• FaceResult contains bboxCorners, mesh, irises and landmarks.
• face.landmarks is a Map<FaceIndex, Point<double>>, where FaceIndex is one of:
  • FaceIndex.leftEye
  • FaceIndex.rightEye
  • FaceIndex.noseTip
  • FaceIndex.mouth
  • FaceIndex.leftEyeTragion
  • FaceIndex.rightEyeTragion
• All coordinates are absolute pixel positions, ready to use for drawing or measurement.

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Example

The example/ directory includes a minimal Flutter app that demonstrates how to paint detections onto an
image: drawing face bounding boxes, the 468-point face mesh, and iris landmarks.
Because results are already in pixel space, overlays align directly with the source image without any extra scaling.