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所在平台: Udemy |
课程主页: https://www.udemy.com/course/face-recognition-detection-in-flutter-2023-guide/
课程评论：没有评论
课程名称：Flutter人脸识别 - 构建考勤与安全应用 课程概述：更新于2024年12月 - 所有库和代码已全面更新为最新的Flutter和TensorFlow Lite版本。本课程是一个全面的实践指南，利用AI和人脸识别技术在移动应用中发挥作用，适合初学者和中级Flutter开发者。课程内容涵盖了从基础的人脸检测和识别到使用TensorFlow Lite、ML Kit以及设备摄像头构建功能齐全的实际应用。 课程内容包括： - 人脸识别系统的工作原理（人脸检测 + 人脸匹配） - 利用图像和实时摄像头输入进行人脸注册和存储 - 使用AI模型如FaceNet和Mobile FaceNet进行人脸识别 - 在Flutter中使用摄像头插件进行实时人脸检测和识别 - 从画廊选择图像与摄像头集成 - 使用TensorFlow Lite模型进行设备端处理 - 在Flutter中利用Google的ML Kit进行人脸检测 - 实施基于人脸的身份验证系统 - 构建安全、考勤和用户验证的实际应用 您将构建的实际应用： - 人脸识别登录应用（通过摄像头进行身份验证） - 学校和工作场所的考勤追踪应用 - 带有实时检测和识别的监控风格应用 - 带有用户注册和姓名映射的人脸数据库管理 涵盖的技术和工具： - Flutter（跨平台移动框架） - TensorFlow Lite（用于在设备上运行ML模型） - MobileFaceNet和FaceNet模型（预训练的人脸识别模型） - ML Kit人脸检测（Google的快速可靠API） - 摄像头插件和图像选择器（轻松捕捉和加载图像） 适合对象： - 有兴趣将机器学习集成到应用中的Flutter开发人员 - 希望构建人脸识别移动应用的AI爱好者 - 开发安全登录/身份验证系统的应用开发者 - 对于具有实际用处的AI驱动相机应用感兴趣的人员 学习成果： 完成课程后，您将能够在iOS和Android上构建和部署AI驱动的人脸识别应用，实时使用摄像头视频进行TensorFlow Lite模型识别，在图像和视频帧中检测和识别面孔，创建基于人脸的用户验证和考勤应用，并掌握Flutter中的图像输入管道和实时处理技能。 不要错过这个机会，掌握在今天的AI驱动移动开发中必不可少的人脸识别技能。立即注册，开始构建强大而智能的应用，让您的作品脱颖而出！
Update December 2024 - All libraries and code fully updated for the latest Flutter and TensorFlow Lite versions.Unlock the power of AI and facial recognition in your mobile apps with this complete hands-on guide to Face Recognition in Flutter! Whether you're a beginner or intermediate Flutter developer, this course will take you from understanding the basics of face detection and recognition to building fully functional, real-world applications using TensorFlow Lite, ML Kit, and the device camera.What You'll Learn:How Face Recognition Systems Work (Face Detection + Face Matching)Face Registration and Storage using Images & Live Camera InputFace Recognition using AI Models like FaceNet and Mobile FaceNetReal-time Face Detection & Recognition in Flutter using Camera PluginImage Selection from Gallery & Camera IntegrationUse of TensorFlow Lite Models for On-Device ProcessingFace Detection using Google's ML Kit in FlutterImplementing Face-Based Authentication SystemsBuild Real Apps for Security, Attendance, and User VerificationReal-World Applications You'll Build:Face Recognition Login App (Authentication via Camera)Attendance Tracking App for schools and workplacesSurveillance-Style App with real-time detection and recognitionFace Database Management with user registration & name mappingTechnologies & Tools Covered:Flutter (Cross-platform mobile framework)TensorFlow Lite (For running ML models on-device)MobileFaceNet & FaceNet Models (Pre-trained models for recognition)ML Kit Face Detection (Google's fast and reliable API)Camera Plugin & Image Picker (Capture & load images easily)Who Should Enroll?Flutter Developers interested in integrating Machine LearningAI Enthusiasts looking to build Face Recognition mobile appsApp Developers building secure login/authentication systemsAnyone interested in AI-powered camera apps with real-world utilityBy the End of This Course, You Will Be Able To:Build and deploy AI-powered Face Recognition apps on iOS & AndroidUse TensorFlow Lite models in real-time with live camera footageDetect and recognize faces in both images and video framesCreate face-based user verification and attendance appsMaster image input pipelines and real-time processing in FlutterDon't miss this opportunity to master face recognition in Flutter, a must-have skill in today's AI-driven mobile development landscape. Enroll now and start building powerful, intelligent apps that stand out!
