update

Author: knottx

CHANGELOG.md

@@ -1,3 +1,9 @@
+## 1.1.0
+
+- Can config use GPU
+- Can config CPU threads
+- Small improvements
+
 ## 1.0.0
 
 - Update Sigmoid score (probability of class=1)

README.md

@@ -75,7 +75,7 @@ void dispose() {
 ```txt
 Live: true
 Score (P(spoof)): 0.023
-Clarity (laplacian): 5939.0
+Clarity (laplacian): 10239.0
 Inference time: 66 ms
 ```
 
@@ -87,15 +87,15 @@ You can tune thresholds:
 final liveness = await FlutterLiveness.create(
   options: LivenessOptions(
     threshold: 0.5, // sigmoid cutoff
-    laplacianThreshold: 1000, // blur block
+    laplacianThreshold: 4000, // blur block
   ),
 );
 ```
 
 ## ❗ Usage Notes
 
 - Feed cropped face images (not full camera frames)
-- Calls are fast (~20–35ms on modern devices)
+- Calls are fast (~60–70ms on modern devices)
 - Throttle analysis: ~3–5 FPS recommended
 - Do not consider this as the only security mechanism for KYC/financial apps — use it as one signal in a trust pipeline
 

example/.gitignore

@@ -43,3 +43,5 @@ app.*.map.json
 /android/app/debug
 /android/app/profile
 /android/app/release
+
+/assets/sample_face_*.jpg

example/assets/sample_face_1.jpg

@@ -16,7 +16,10 @@ class FlutterLiveness {
   static Future<FlutterLiveness> create({
     LivenessOptions options = const LivenessOptions(),
   }) async {
-    final runner = await TFLiteRunner.create(useGpu: options.useGpu);
+    final runner = await TFLiteRunner.create(
+      useGpu: options.useGpu,
+      threads: options.threads,
+    );
     final engine = LivenessEngine(runner, options);
     return FlutterLiveness._(engine, runner);
   }

example/assets/sample_face_2.jpg

@@ -7,10 +7,9 @@ List<List<List<List<double>>>> toNHWC(img.Image resized) {
     List.generate(
       224,
       (_) => List.generate(224, (_) {
-        final r = bytes[index++].toInt() / 255.0;
-        final g = bytes[index++].toInt() / 255.0;
-        final b = bytes[index++].toInt() / 255.0;
-
+        final r = bytes[index++] / 255.0;
+        final g = bytes[index++] / 255.0;
+        final b = bytes[index++] / 255.0;
         return [r, g, b];
       }),
     ),

example/assets/sample_face_3.jpg

@@ -5,23 +5,27 @@ int laplacianScore(
   required int laplacePixelThreshold,
 }) {
   final gray = img.grayscale(resized);
-  const kernel = [
-    [0, 1, 0],
-    [1, -4, 1],
-    [0, 1, 0],
-  ];
-  var score = 0;
+  final bytes = gray.getBytes(order: img.ChannelOrder.red);
 
-  for (var r = 0; r <= gray.height - 3; r++) {
-    for (var c = 0; c <= gray.width - 3; c++) {
-      var conv = 0;
-      for (var kr = 0; kr < 3; kr++) {
-        for (var kc = 0; kc < 3; kc++) {
-          final px = gray.getPixel(c + kc, r + kr);
-          conv += px.r.toInt() * kernel[kr][kc];
-        }
+  int score = 0;
+
+  for (int x = 1; x < 224 - 2; x++) {
+    for (int y = 1; y < 224 - 2; y++) {
+      final int index = x * 224 + y;
+
+      final int center = bytes[index];
+
+      final int north = bytes[index - 224];
+      final int south = bytes[index + 224];
+      final int east = bytes[index + 1];
+      final int west = bytes[index - 1];
+
+      // Conv = N + S + E + W - 4*Center
+      final int conv = (north + south + east + west) - (4 * center);
+
+      if (conv.abs() > laplacePixelThreshold) {
+        score++;
       }
-      if (conv.abs() > laplacePixelThreshold) score++;
     }
   }
   return score;

lib/src/flutter_liveness.dart

@@ -3,36 +3,40 @@ import 'package:flutter_liveness/src/laplacian.dart';
 import 'package:flutter_liveness/src/liveness_options.dart';
 import 'package:flutter_liveness/src/liveness_result.dart';
 import 'package:flutter_liveness/src/tflite_runner.dart';
-import 'package:image/image.dart' as imglib;
+import 'package:image/image.dart' as img;
 
 class LivenessEngine {
   final TFLiteRunner _runner;
-  final LivenessOptions _opt;
+  final LivenessOptions _options;
 
   const LivenessEngine(
     this._runner,
-    this._opt,
+    this._options,
   );
 
-  Future<LivenessResult> analyze(imglib.Image face) async {
-    final resized = imglib.copyResize(
-      face,
-      width: 224,
-      height: 224,
-    );
+  Future<LivenessResult> analyze(img.Image face) async {
+    final t0 = DateTime.now();
+
+    final img.Image resized;
+    if (face.width == 224 && face.height == 224) {
+      resized = face;
+    } else {
+      resized = img.copyResize(face, width: 224, height: 224);
+    }
 
     final int laplacian;
-    if (_opt.applyLaplacianGate) {
+    if (_options.applyLaplacianGate) {
       laplacian = laplacianScore(
         resized,
-        laplacePixelThreshold: _opt.laplacePixelThreshold,
+        laplacePixelThreshold: _options.laplacePixelThreshold,
       );
     } else {
       /// Bypass laplacian calculation
       laplacian = 999999;
     }
 
-    if (_opt.applyLaplacianGate && laplacian < _opt.laplacianThreshold) {
+    if (_options.applyLaplacianGate &&
+        laplacian < _options.laplacianThreshold) {
       return LivenessResult(
         isLive: false,
         score: 1,
@@ -41,21 +45,21 @@ class LivenessEngine {
       );
     }
 
-    final input = toNHWC(face);
-    final t0 = DateTime.now();
+    final input = toNHWC(resized);
 
     /// Probability of class=1 (spoof)
     final prob1 = await _runner.inferProb(input);
 
-    final dt = DateTime.now().difference(t0);
     final score = (1.0 - prob1);
-    final isLive = score >= _opt.threshold;
+    final isLive = score >= _options.threshold;
+
+    final duration = DateTime.now().difference(t0);
 
     return LivenessResult(
       isLive: isLive,
       score: prob1,
       laplacian: laplacian,
-      duration: dt,
+      duration: duration,
     );
   }
 }