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北京企业网站开发,今日疫情最新消息,wordpress 轮播图插件,建设工程行业招工信息网站基于MLKit的Android人脸识别应用开发实践 https://gitee.com/wenhua512/face-recognition 1. 项目概述 1.1 功能特点实时人脸检测与跟踪人脸特征提取与识别自动/手动采集模式人脸数据管理相机参数优化 1.2 技术选型 MLKit人脸检测MediaPipe人脸网格CameraX相机框架Room数…

基于MLKit的Android人脸识别应用开发实践

https://gitee.com/wenhua512/face-recognition

1. 项目概述

1.1 功能特点

实时人脸检测与跟踪
人脸特征提取与识别
自动/手动采集模式
人脸数据管理
相机参数优化

1.2 技术选型

MLKit人脸检测
MediaPipe人脸网格
CameraX相机框架
Room数据库
Kotlin协程

2. 核心实现

2.1 人脸检测配置

private fun createFaceDetector(): FaceDetector {val options = FaceDetectorOptions.Builder().setPerformanceMode(FaceDetectorOptions.PERFORMANCE_MODE_FAST).setContourMode(FaceDetectorOptions.CONTOUR_MODE_ALL).setLandmarkMode(FaceDetectorOptions.LANDMARK_MODE_ALL).setClassificationMode(FaceDetectorOptions.CLASSIFICATION_MODE_ALL).setMinFaceSize(0.15f).build()return FaceDetection.getClient(options)
}

2.2 相机预览实现

private fun startCamera() {val cameraProviderFuture = ProcessCameraProvider.getInstance(this)cameraProviderFuture.addListener({val cameraProvider = cameraProviderFuture.get()val preview = Preview.Builder().setTargetResolution(targetResolution).build().also {it.setSurfaceProvider(binding.viewFinder.surfaceProvider)}val imageAnalyzer = ImageAnalysis.Builder().setTargetResolution(targetResolution).setBackpressureStrategy(ImageAnalysis.STRATEGY_BLOCK_PRODUCER).build().also {it.setAnalyzer(cameraExecutor, FaceAnalyzer())}val cameraSelector = CameraSelector.DEFAULT_FRONT_CAMERAtry {cameraProvider.unbindAll()cameraProvider.bindToLifecycle(this, cameraSelector, preview, imageAnalyzer)} catch(exc: Exception) {Log.e(TAG, "相机绑定失败", exc)}}, ContextCompat.getMainExecutor(this))
}

2.3 人脸特征提取

private suspend fun extractFaceFeatures(bitmap: Bitmap): FloatArray? {return withContext(Dispatchers.IO) {try {val faceBitmap = cropFace(bitmap, faceMesh.boundingBox)val processedBitmap = ImagePreprocessor.process(faceBitmap,shouldNormalizeLighting = true,shouldEnhanceContrast = true,shouldEqualizeHistogram = true)faceFeatureExtractor.extractFeatures(processedBitmap)} catch (e: Exception) {Log.e(TAG, "特征提取失败", e)null}}
}

3. 性能优化

3.1 图像处理优化

使用YUV格式处理图像
实现图像缓存机制
优化人脸裁剪算法

3.2 内存管理

private fun cleanupResources() {try {if (::cameraExecutor.isInitialized && !cameraExecutor.isShutdown) {cameraExecutor.shutdown()}currentFaceBitmap?.recycle()currentFaceBitmap = nulllatestBitmap?.recycle()latestBitmap = nullcollectedFeatures.clear()} catch (e: Exception) {Log.e(TAG, "资源清理失败", e)}
}

3.3 多线程处理

private val processingMutex = Mutex()
private val processIntervalMs = 1000Lprivate suspend fun processFrame(bitmap: Bitmap) {if (!processingMutex.tryLock()) returntry {val currentTime = System.currentTimeMillis()if (currentTime - lastProcessTime < processIntervalMs) return// 处理逻辑lastProcessTime = currentTime} finally {processingMutex.unlock()}
}

4. 用户体验优化

4.1 采集模式切换

private fun updateCollectionModeUI() {binding.apply {autoCollectLayout.visibility = if (isAutoCollectMode) View.VISIBLE else View.GONEmanualCollectLayout.visibility = if (isAutoCollectMode) View.GONE else View.VISIBLEif (isAutoCollectMode) {tvStatus.text = "自动采集模式：请保持人脸在框内..."} else {Toast.makeText(this@FaceAutoCollectActivity, "手动采集模式：请保持人脸在框内...", Toast.LENGTH_SHORT).show()}}
}

4.2 人脸质量检测

private fun isValidFaceMesh(faceMesh: FaceMesh, imageProxy: ImageProxy): Boolean {// 检查人脸角度val faceAngle = calculateFaceAngle(faceMesh)if (abs(faceAngle) > MAX_FACE_ANGLE) {showHint("请保持正面朝向摄像头")return false}// 检查人脸大小val faceSize = calculateFaceSize(faceMesh, imageProxy)if (faceSize < MIN_FACE_SIZE || faceSize > MAX_FACE_SIZE) {showHint("请调整与摄像头的距离")return false}return true
}