face_detection_tflite 1.0.1

Face & landmark detection using on-device TFLite models.

face_detection_tflite

A pure Dart/Flutter implementation of Google's MediaPipe face detection and facial landmark models using TensorFlow Lite. This package provides on-device face and landmark detection with minimal dependencies, just TensorFlow Lite and image.

Bounding Box Example:

[Example Screenshot]

Mesh (468-Point) Example:

[Example Screenshot]

Landmark Example:

[Example Screenshot]

Iris Example:

[Example Screenshot]

Table of Contents

• Features
• Quick Start
• Models
• Types
• Example
• Inspiration

Features

• On-device face detection (multiple SSD variants)
• 468-point face mesh, face landmarks, iris landmarks and bounding boxes
• All coordinates are in absolute pixel coordinates (Point<double>)
  • x ranges from 0 to image.width
  • y ranges from 0 to image.height
  • Ready to use co-ordinates without any scaling
• 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.

Quick Start

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

Future main() async {
  // 1. initialize
  final detector = FaceDetector();
  await detector.initialize(model: FaceDetectionModel.backCamera);

  // 2. detect
  final imageBytes = await File('path/to/image.jpg').readAsBytes();
  final result = await detector.detectFaces(imageBytes);

  // 3. access results
  for (final face in result.faces) {
    final landmarks = face.landmarks;
    final bbox = face.bboxCorners;  
    final mesh = face.mesh;     
    final irises = face.irises;
    
    final leftEye = landmarks[FaceIndex.leftEye];
    final rightEye = landmarks[FaceIndex.rightEye];

    print('Left eye: (${leftEye.x}, ${leftEye.y})');
  }

  // 4. clean-up
  detector.dispose();
}

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).

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.

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.

Inspiration

At the time of development, there was no open-source solution for cross-platform, on-device face and landmark detection. This package took inspiration and was ported from the original Python project patlevin/face-detection-tflite. Many thanks to the original author.