face_detection_tflite 5.0.3

Advanced face & landmark detection, embedding and segmentation using on-device TFLite models.

face_detection_tflite

Flutter implementation of Google's MediaPipe face and facial landmark detection models using TensorFlow Lite. Completely local: no remote API, just pure on-device, offline detection.

Bounding Boxes

Facial Mesh (468-Point)

Facial Landmarks

Eye Tracking

Iris Detection:

Eye Area Mesh (71-Point):

Note: The Facial mesh and eye area mesh are separate.

Eye Contour:

Features

• On-device face detection, runs fully offline
• Face recognition: 192-dim embeddings to identify/compare faces across images
• Selfie segmentation: separate person from background, or use multiclass model for 6-class body part segmentation (hair, face, body, clothes, etc.)
• 468 point mesh with 3D depth information (x, y, z coordinates)
• Face landmarks, comprehensive eye tracking (iris + 71-point eye mesh), and bounding boxes
• All coordinates are in absolute pixel coordinates
• Truly cross-platform: compatible with Android, iOS, macOS, Windows, and Linux
• Native OpenCV preprocessing (resize/letterbox/crops) for 2x+ throughput vs pure Dart
• The example app illustrates how to detect and render results on images
  • Includes demo for bounding boxes, the 468-point mesh, facial landmarks and comprehensive eye tracking.

Quick Start

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

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

  final imageBytes = await File('path/to/image.jpg').readAsBytes();
  List<Face> faces = await detector.detectFaces(imageBytes);

  for (final face in faces) {
    final boundingBox = face.boundingBox;
    final landmarks   = face.landmarks;
    final mesh = face.mesh;
    final eyes = face.eyes;
  }

  detector.dispose();
}

Performance

Version 4.1 moved image preprocessing to native OpenCV (via opencv_dart) for ~2x faster performance with SIMD acceleration. The standard detectFaces() method now uses OpenCV internally, so all existing code automatically gets the performance boost.

Hardware Acceleration

The package automatically selects the best acceleration strategy for each platform:

Platform	Default Delegate	Speedup	Notes
macOS	XNNPACK	2-5x	SIMD vectorization (NEON on ARM, AVX on x86)
Linux	XNNPACK	2-5x	SIMD vectorization
iOS	Metal GPU	2-4x	Hardware GPU acceleration
Android	CPU	1x	GPU delegate unreliable (see below)
Windows	CPU	1x	XNNPACK crashes on Windows

No configuration needed - just call initialize() and you get the optimal performance for your platform.

Android Performance Note

The Android GPU delegate has known compatibility issues across different devices and Android versions:

• OpenCL unavailable on many devices (Pixel 6+, Android 12+)
• OpenGL ES 3.1+ required for fallback
• Some devices crash during GPU delegate initialization

For maximum compatibility, Android defaults to CPU-only execution. If you want to experiment with GPU acceleration on Android (at your own risk), see the Advanced Configuration section.

Advanced Performance Configuration

// Auto mode (default) - optimal for each platform
await detector.initialize();
// Equivalent to:
await detector.initialize(performanceConfig: PerformanceConfig.auto());

// Force XNNPACK (desktop only - macOS/Linux)
await detector.initialize(
  performanceConfig: PerformanceConfig.xnnpack(numThreads: 4),
);

// Force GPU delegate (iOS recommended, Android experimental)
await detector.initialize(
  performanceConfig: PerformanceConfig.gpu(),
);

// CPU-only (maximum compatibility)
await detector.initialize(
  performanceConfig: PerformanceConfig.disabled,
);

Advanced: Direct Mat Input

For live camera streams, you can bypass image encoding/decoding entirely by passing a cv.Mat directly to detectFaces():

import 'package:face_detection_tflite/face_detection_tflite.dart';

Future<void> processFrame(cv.Mat frame) async {
  final detector = FaceDetector();
  await detector.initialize(model: FaceDetectionModel.frontCamera);

  // Direct Mat input - fastest for video streams
  final faces = await detector.detectFacesFromMat(frame, mode: FaceDetectionMode.fast);

  frame.dispose(); // always dispose Mats after use
  detector.dispose();
}

When to use cv.Mat input:

• Live camera streams where frames are already in memory
• When you need to preprocess images with OpenCV before detection
• Maximum throughput scenarios (avoids JPEG encode/decode overhead)

For all other cases, pass image bytes (Uint8List) to detectFaces().

Bounding Boxes

The boundingBox property returns a BoundingBox object representing the face bounding box in absolute pixel coordinates. The BoundingBox provides convenient access to corner points, dimensions (width and height), and the center point.

Accessing Corners

final BoundingBox boundingBox = face.boundingBox;

// Access individual corners by name (each is a Point with x and y)
final Point topLeft     = boundingBox.topLeft;       // Top-left corner
final Point topRight    = boundingBox.topRight;      // Top-right corner
final Point bottomRight = boundingBox.bottomRight;   // Bottom-right corner
final Point bottomLeft  = boundingBox.bottomLeft;    // Bottom-left corner

// Access coordinates
print('Top-left: (${topLeft.x}, ${topLeft.y})');

Additional Bounding Box Parameters

final BoundingBox boundingBox = face.boundingBox;

// Access dimensions and center
final double width  = boundingBox.width;     // Width in pixels
final double height = boundingBox.height;    // Height in pixels
final Point center = boundingBox.center;  // Center point

// Access coordinates
print('Size: ${width} x ${height}');
print('Center: (${center.x}, ${center.y})');

// Access all corners as a list (order: top-left, top-right, bottom-right, bottom-left)
final List<Point> allCorners = boundingBox.corners;

Landmarks

The landmarks property returns a FaceLandmarks object with 6 key facial feature points in absolute pixel coordinates. These landmarks provide quick access to common facial features with convenient named properties.

Accessing Landmarks

final FaceLandmarks landmarks = face.landmarks;

// Access individual landmarks using named properties
final leftEye  = landmarks.leftEye;
final rightEye = landmarks.rightEye;
final noseTip  = landmarks.noseTip;
final mouth    = landmarks.mouth;
final leftEyeTragion  = landmarks.leftEyeTragion;
final rightEyeTragion = landmarks.rightEyeTragion;

// Access coordinates
print('Left eye: (${leftEye?.x}, ${leftEye?.y})');
print('Nose tip: (${noseTip?.x}, ${noseTip?.y})');

// Iterate through all landmarks
for (final point in landmarks.values) {
  print('Landmark: (${point.x}, ${point.y})');
}

Face Mesh

The mesh property returns a FaceMesh object containing 468 facial landmark points with both 2D and 3D coordinate access. These points map to specific facial features and can be used for precise face tracking and rendering.

Accessing Mesh Points

import 'package:face_detection_tflite/face_detection_tflite.dart';

final FaceMesh? mesh = face.mesh;

if (mesh != null) {
  // Get mesh points
  final points = mesh.points;

  // Total number of points (always 468)
  print('Mesh points: ${points.length}');

  // Iterate through all points (all mesh points have z-coordinates)
  for (int i = 0; i < points.length; i++) {
    final point = points[i];
    print('Point $i: (${point.x}, ${point.y}, ${point.z})');
  }

  // Access individual points using index operator
  final noseTip = mesh[1];     // Nose tip point
  final leftEye = mesh[33];    // Left eye point
  final rightEye = mesh[263];  // Right eye point
}

Accessing 3D Depth Information

All face mesh points include x, y, and z coordinates. The z coordinate represents relative depth (scale-dependent). 3D coordinates are always computed for mesh and iris landmarks.

import 'package:face_detection_tflite/face_detection_tflite.dart';

final FaceMesh? mesh = face.mesh;

if (mesh != null) {
  // Get all points
  final points = mesh.points;

  // Iterate through all points (all mesh points have x, y, and z)
  for (final point in points) {
    print('Point: (${point.x}, ${point.y}, ${point.z})');
  }

  // Access individual points directly using index operator
  final noseTip = mesh[1];
  print('Nose tip depth: ${noseTip.z}');
}

Eye Tracking (Iris + Eye Mesh)

The eyes property returns comprehensive eye tracking data for both eyes in absolute pixel coordinates. Each eye includes:

• Iris center (irisCenter): The iris center point
• Iris contour (irisContour): 4 points outlining the iris boundary
• Contour (contour): 15 points outlining the eyelid
• Mesh (mesh): 71 landmarks covering the entire eye region

Only available in FaceDetectionMode.full.

Accessing Eye Data

final EyePair? eyes = face.eyes;

// Access left and right eye data (each is an Eye object containing all eye info)
final Eye? leftEye = eyes?.leftEye;
final Eye? rightEye = eyes?.rightEye;

if (leftEye != null) {
  // Access iris center
  final irisCenter = leftEye.irisCenter;
  print('Left iris center: (${irisCenter.x}, ${irisCenter.y})');

  // Access iris contour points (4 points outlining the iris)
  for (final point in leftEye.irisContour) {
    print('Iris contour: (${point.x}, ${point.y})');
  }

  // Access eye mesh landmarks (71 points covering the entire eye region)
  for (final point in leftEye.mesh) {
    print('Eye mesh point: (${point.x}, ${point.y})');
  }

  // Access just the eyelid contour (first 15 points of the eye mesh)
  for (final point in leftEye.contour) {
    print('Eyelid contour: (${point.x}, ${point.y})');
  }
}

// Right eye works the same way
if (rightEye != null) {
  final irisCenter = rightEye.irisCenter;
  print('Right iris center: (${irisCenter.x}, ${irisCenter.y})');
}

Rendering Eye Contours

For rendering the visible eyelid outline, use the contour getter and connect them using eyeLandmarkConnections:

import 'package:face_detection_tflite/face_detection_tflite.dart';

// Get the visible eyeball contour (first 15 of 71 points)
final List<Point> eyelidOutline = leftEye.contour;

// Draw the eyelid outline by connecting the points
for (final connection in eyeLandmarkConnections) {
  final p1 = eyelidOutline[connection[0]];
  final p2 = eyelidOutline[connection[1]];
  canvas.drawLine(
    Offset(p1.x, p1.y),
    Offset(p2.x, p2.y),
    paint,
  );
}

Face Detection Modes

This package supports three detection modes that determine which facial features are detected:

Mode	Features	Est. Time per Face*
Full (default)	Bounding boxes, landmarks, 468-point mesh, eye tracking (iris + 71-point eye mesh)	~80-120ms
Standard	Bounding boxes, landmarks, 468-point mesh	~60ms
Fast	Bounding boxes, landmarks	~30ms

*Est. times per faces are based on 640x480 resolution on modern hardware. Performance scales with image size and number of faces.

Code Examples

The Face Detection Mode can be set using the mode parameter. Defaults to FaceDetectionMode.full.

// Full mode (default): bounding boxes, 6 basic landmarks + mesh + comprehensive eye tracking
// note: in full mode, landmarks.leftEye and landmarks.rightEye are replaced with
// iris-refined coordinates, providing significantly more accurate eye positions
// compared to the raw detection keypoints used in fast/standard modes.
// use full mode when precise eye tracking (iris center, contour, eyelid shape) is required.
await faceDetector.detectFaces(bytes, mode: FaceDetectionMode.full);

// Standard mode: bounding boxes, 6 basic landmarks + mesh. inference time
// is faster than full mode, but slower than fast mode.
await faceDetector.detectFaces(bytes, mode: FaceDetectionMode.standard);

// Fast mode: bounding boxes + 6 basic landmarks only. fastest inference
// time of the three modes.
await faceDetector.detectFaces(bytes, mode: FaceDetectionMode.fast);

Try the sample code from the pub.dev example tab to easily compare modes and inferences timing.

Models

This package supports multiple detection models optimized for different use cases:

Model	Best For
backCamera (default)	Group shots, distant faces, rear camera
frontCamera	Selfies, close-up portraits, front camera
shortRange	Close-range faces (within ~2m)
full	Mid-range faces (within ~5m)
fullSparse	Mid-range faces with faster inference (~30% speedup)

Code Examples

The model can be set using the model parameter when initialize is called. Defaults to FaceDetectionModel.backCamera.

FaceDetector faceDetector = FaceDetector();

// backCamera (default): larger model for group shots or images with smaller faces
await faceDetector.initialize(model: FaceDetectionModel.backCamera);

// frontCamera: optimized for selfies and close-up portraits
await faceDetector.initialize(model: FaceDetectionModel.frontCamera);

// shortRange: best for short-range images (faces within ~2m)
await faceDetector.initialize(model: FaceDetectionModel.shortRange);

// full: best for mid-range images (faces within ~5m)
await faceDetector.initialize(model: FaceDetectionModel.full);

// fullSparse: same detection quality as full but runs up to 30% faster on CPU
// (slightly higher precision, slightly lower recall)
await faceDetector.initialize(model: FaceDetectionModel.fullSparse);

Live Camera Detection

For real-time face detection with a camera feed, pass a cv.Mat directly to detectFacesFromMat() to avoid repeated JPEG encode/decode overhead. This provides the best performance for video streams.

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

FaceDetector detector = FaceDetector();
await detector.initialize(model: FaceDetectionModel.frontCamera);

final cameras = await availableCameras();
CameraController camera = CameraController(cameras.first, ResolutionPreset.medium);
await camera.initialize();

camera.startImageStream((CameraImage image) async {
  // Convert CameraImage (YUV420) directly to cv.Mat (BGR)
  final cv.Mat mat = convertCameraImageToMat(image); // see example app

  // Detect faces using Mat for maximum performance
  List<Face> faces = await detector.detectFacesFromMat(
    mat,
    mode: FaceDetectionMode.fast,
  );

  // Always dispose Mat after use
  mat.dispose();

  // Process faces...
});

Tips for camera detection:

• Pass cv.Mat directly to detectFacesFromMat() to bypass JPEG encoding/decoding
• Convert YUV420 camera frames directly to BGR Mat format
• Always call mat.dispose() after detection
• Use FaceDetectionMode.fast for real-time performance

See the full example app for complete implementation including YUV-to-Mat conversion and frame throttling.

Background Isolate Detection

For applications that require guaranteed non-blocking UI, use FaceDetectorIsolate. This runs the entire detection pipeline in a background isolate, ensuring all processing happens off the main thread.

import 'package:face_detection_tflite/face_detection_tflite.dart';

// Spawn isolate (loads models in background)
final detector = await FaceDetectorIsolate.spawn();

// All detection runs in background isolate - UI never blocked
final faces = await detector.detectFaces(imageBytes);

for (final face in faces) {
  print('Face at: ${face.boundingBox.center}');
  print('Mesh points: ${face.mesh?.length ?? 0}');
}

// Cleanup when done
await detector.dispose();

When to Use FaceDetectorIsolate

Use Case	Recommended
Live camera with 60fps UI requirement	FaceDetectorIsolate
Processing images in a batch queue	FaceDetectorIsolate
Simple single-image detection	FaceDetector
Maximum control over pipeline stages	FaceDetector

Configuration

FaceDetectorIsolate.spawn() accepts the same configuration options as FaceDetector.initialize(), except for InterpreterOptions:

final detector = await FaceDetectorIsolate.spawn(
  model: FaceDetectionModel.frontCamera,
  performanceConfig: PerformanceConfig.auto(), // Or .gpu() for iOS
  meshPoolSize: 2,
);

OpenCV Mat Support

FaceDetectorIsolate fully supports OpenCV cv.Mat input, ideal for live camera processing:

import 'package:opencv_dart/opencv_dart.dart' as cv;

// From cv.Mat (e.g., decoded image or camera frame)
final mat = cv.imdecode(imageBytes, cv.IMREAD_COLOR);
final faces = await detector.detectFacesFromMat(mat);
mat.dispose();

// From raw BGR bytes (e.g., converted camera YUV)
final faces = await detector.detectFacesFromMatBytes(
  bgrBytes,
  width: frameWidth,
  height: frameHeight,
);

The Mat is reconstructed in the background isolate using zero-copy transfer, so there's no encoding/decoding overhead.

Face Recognition (Embeddings)

Generate 192-dimensional identity vectors to compare faces across images. Useful for identifying the same person in different photos.

final detector = FaceDetector();
await detector.initialize();

// Get reference embedding from a photo with one face
final refFaces = await detector.detectFaces(photo1Bytes, mode: FaceDetectionMode.fast);
final refEmbedding = await detector.getFaceEmbedding(refFaces.first, photo1Bytes);

// Compare against faces in another photo
final faces = await detector.detectFaces(photo2Bytes, mode: FaceDetectionMode.fast);
for (final face in faces) {
  final embedding = await detector.getFaceEmbedding(face, photo2Bytes);
  final similarity = FaceDetector.compareFaces(refEmbedding, embedding);
  print('Similarity: ${similarity.toStringAsFixed(2)}'); // -1.0 to 1.0
}

detector.dispose();

Similarity thresholds:

• > 0.6 — Very likely same person
• > 0.5 — Probably same person
• < 0.3 — Different people

Also available: FaceDetector.faceDistance() for Euclidean distance, and batch processing with getFaceEmbeddings().

Selfie Segmentation

Separate people from backgrounds using MediaPipe Selfie Segmentation. Useful for virtual backgrounds, portrait effects, and background blur.

Standalone Usage

import 'package:face_detection_tflite/face_detection_tflite.dart';

final segmenter = await SelfieSegmentation.create();

final mask = await segmenter.callFromBytes(imageBytes);

// mask.width, mask.height - mask dimensions (model resolution)
// mask.at(x, y) - probability (0.0-1.0) that pixel is a person

// Convert to binary mask (0 or 255)
final binary = mask.toBinary(threshold: 0.5);

// Convert to grayscale (0-255)
final grayscale = mask.toUint8();

// Upsample to original image size
final fullSize = mask.upsample();

segmenter.dispose();

With FaceDetector

final detector = FaceDetector();
await detector.initialize();

// Defaults to SegmentationConfig.safe (CPU-only, 1024 max output).
// On iOS/desktop, use SegmentationConfig.performance for hardware acceleration.
await detector.initializeSegmentation();

final mask = await detector.getSegmentationMask(imageBytes);
// Use mask for background replacement...

detector.dispose();

With FaceDetectorIsolate

final detector = await FaceDetectorIsolate.spawn(
  withSegmentation: true,
  segmentationConfig: SegmentationConfig(model: SegmentationModel.general),
);

final mask = await detector.getSegmentationMask(imageBytes);
// Or from cv.Mat for camera streams:
final mask = await detector.getSegmentationMaskFromMat(mat);

await detector.dispose();

Model Variants

Model	Input Size	Output	Best For
general (default)	256×256	Binary	Portraits, square images
landscape	144×256	Binary	Wide images, video streams
multiclass	256×256	6 classes	Body part segmentation

// Use landscape model for video
final segmenter = await SelfieSegmentation.create(
  config: SegmentationConfig(model: SegmentationModel.landscape),
);

// Use multiclass for body part segmentation
final segmenter = await SelfieSegmentation.create(
  config: SegmentationConfig(model: SegmentationModel.multiclass),
);

Multiclass Segmentation

The multiclass model segments images into 6 body part classes:

Class Index	Class Name	Description
0	Background	Non-person pixels
1	Hair	Hair regions
2	Body Skin	Arms, hands, legs (exposed skin)
3	Face Skin	Face and neck skin
4	Clothes	Clothing regions
5	Other	Accessories, hats, glasses, etc.

final segmenter = await SelfieSegmentation.create(
  config: SegmentationConfig(model: SegmentationModel.multiclass),
);

final mask = await segmenter.callFromBytes(imageBytes);

// Check if we got a multiclass mask
if (mask is MulticlassSegmentationMask) {
  // Access individual class probability masks
  final hairMask = mask.hairMask;           // Float32List of probabilities
  final faceSkinMask = mask.faceSkinMask;
  final bodySkinMask = mask.bodySkinMask;
  final clothesMask = mask.clothesMask;
  final backgroundMask = mask.backgroundMask;
  final otherMask = mask.otherMask;

  // Or access by index
  final hairMask2 = mask.classMask(1);  // Same as hairMask

  // The base mask.data still contains combined person probability
  final combinedPerson = mask.at(x, y);
}

segmenter.dispose();

Memory Considerations

The background isolate holds all TFLite models (~26-40MB for full pipeline). Always call dispose() when finished to release these resources. Image data is transferred using zero-copy TransferableTypedData, minimizing memory overhead.

Example

The sample code from the pub.dev example tab includes a Flutter app demonstrating all features:

Face Detection Demo:

• Bounding boxes, landmarks, 468-point mesh, and comprehensive eye tracking
• Compare FaceDetectionMode.fast, standard, and full modes
• Real-time inference timing display

Selfie Segmentation Demo:

• Switch between general, landscape, and multiclass models
• Visualize individual body part masks (hair, face skin, clothes, etc.) with multiclass
• Adjustable threshold, binary/soft mask toggle, and color options
• Virtual background replacement demo in live camera mode

Running Tests

Integration tests are located in example/integration_test/. Due to a Flutter macOS test runner limitation, tests must be run one file at a time on macOS (running all together causes app launch failures between files).

iOS

cd example
flutter test integration_test/ -d <ios-device-id>

macOS (run each file separately)

cd example

# Kill any existing instances, then run a single test file
pkill -9 -f "face_detection_tflite_example"; sleep 2
flutter test integration_test/face_detection_integration_test.dart -d macos

# Repeat for each test file:
# - opencv_helpers_test.dart (20 tests)
# - performance_config_test.dart (17 tests)
# - face_detection_integration_test.dart (97 tests)
# - embedding_match_test.dart (1 test)
# - gpu_delegate_test.dart (2 tests)
# - benchmark_test.dart (4 tests)
# - error_recovery_test.dart (27 tests)
# - edge_cases_test.dart (33 tests)
# - all_model_variants_test.dart (18 tests)
# - image_utils_test.dart (31 tests)
# - concurrency_stress_test.dart (18 tests)
# - combined_segmentation_test.dart (18 tests)
# - helpers_unit_test.dart (29 tests)
# - assertion_gaps_test.dart (18 tests)
# - selfie_segmentation_test.dart (78 tests)
# - isolate_mat_debug_test.dart (2 tests)

Migrating to 5.0.0

What changed (and why)

Version 5.0.0 removes the package:image dependency from face_detection_tflite.

All image processing (decoding, resizing, cropping, etc.) now uses OpenCV internally, which is significantly faster. This makes the image package unnecessary, so it has been removed.

In practice:

• If you already pass image bytes (Uint8List): no changes needed
• If you already pass a cv.Mat: use the FromMat methods (e.g. detectFacesFromMat())
• If you were passing img.Image objects: those APIs were removed (see fix below)

If you pass image bytes (Uint8List): nothing changes

This is the most common usage and it works exactly the same as before.

From a file

import 'dart:io';

final bytes = await File('photo.jpg').readAsBytes();
final faces = await detector.detectFaces(bytes);

From Flutter assets

import 'package:flutter/services.dart';

final data = await rootBundle.load('assets/images/photo.jpg');
final bytes = data.buffer.asUint8List(data.offsetInBytes, data.lengthInBytes);
final faces = await detector.detectFaces(bytes);

From the network

import 'package:http/http.dart' as http;

final response = await http.get(Uri.parse('https://example.com/photo.jpg'));
final faces = await detector.detectFaces(response.bodyBytes);

If you pass a cv.Mat: use the FromMat methods

If your app already works with OpenCV matrices (for example, camera frames), use the FromMat variant of each method:

import 'package:face_detection_tflite/face_detection_tflite.dart';

final mat = imdecode(bytes, IMREAD_COLOR);
final faces = await detector.detectFacesFromMat(mat);
mat.dispose(); // always dispose Mats when you're done

If you were passing img.Image objects

The methods that accepted img.Image (from package:image) have been removed in 5.0.0.

The fix is simple: pass the raw bytes directly instead of decoding to img.Image first.

Before (4.x — no longer works)

import 'package:image/image.dart' as img;

final bytes = await File('photo.jpg').readAsBytes();
final decoded = img.decodeImage(bytes)!;
final faces = await detector.detectFaces(decoded); // removed in 5.0.0

After (5.0.0)

final bytes = await File('photo.jpg').readAsBytes();
final faces = await detector.detectFaces(bytes); // just pass the bytes directly

Still want to use package:image for preprocessing?

If you need to crop, rotate, or otherwise manipulate images with package:image before detection, you can still do that. Just encode the result back to bytes before passing it in:

import 'dart:typed_data';
import 'package:image/image.dart' as img;

final originalBytes = await File('photo.jpg').readAsBytes();
final decoded = img.decodeImage(originalBytes)!;

// Do your preprocessing
final cropped = img.copyCrop(decoded, x: 0, y: 0, width: 300, height: 300);

// Encode back to bytes, then pass to detectFaces
final processedBytes = Uint8List.fromList(img.encodeJpg(cropped));
final faces = await detector.detectFaces(processedBytes);

Separate typed methods

Methods now have typed overloads instead of accepting Object:

Uint8List variant	cv.Mat variant
detectFaces(bytes)	detectFacesFromMat(mat)
getFaceEmbedding(face, bytes)	getFaceEmbeddingFromMat(face, mat)
getSegmentationMask(bytes)	getSegmentationMaskFromMat(mat)

OpenCV re-exports (no extra dependency needed)

You do not need to add opencv_dart to your own pubspec.yaml to use OpenCV types with this package. face_detection_tflite re-exports Mat, imdecode, and IMREAD_COLOR, so this works out of the box:

import 'package:face_detection_tflite/face_detection_tflite.dart';

final mat = imdecode(bytes, IMREAD_COLOR); // no extra import needed
final faces = await detector.detectFacesFromMat(mat);

Model Cards

All TFLite models are sourced from Google's MediaPipe framework. Official model cards are archived in doc/model_cards/:

Model	File	Model Card
Face Mesh (468-point landmark)	face_landmark.tflite	face_landmark_model_card.pdf · mediapipe.page.link/facemesh-mc
Iris Landmark (76-point)	iris_landmark.tflite	iris_landmark_model_card.pdf · mediapipe.page.link/iris-mc

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.