Offline Facial Attendance with Kiosk

@diektech

Hi, This is Elias from Miami. I checked your project description and understand you’re looking to develop an offline facial recognition module alongside a mobile app for both iOS and Android, integrated with a kiosk. This sounds like an innovative solution for attendance management. I have experience in mobile app development and have worked on similar facial recognition projects, so I'm familiar with the technical challenges involved. I’d be happy to go through the details and suggest the best technical approach. I have a few questions to get a better understanding: Q1 – What specific user roles do you envision for the kiosk and app? Q2 – Are there any existing systems or databases we need to integrate with? Q3 – How do you plan to handle user authentication and data security? Looking forward to hearing from you.

@ZohaibRoy

⭐⭐⭐⭐⭐ Build Offline Facial Recognition Module & Mobile App for Kiosk Use ❇️ Hi My Friend, I hope you are doing well. I reviewed your project details and see you are looking for an Offline Facial Recognition Module with mobile app packaging. You don't need to look any further; Zohaib is here to help you! My team has completed over 50 similar projects in facial recognition. I will create a reliable offline system that connects to your on-premise database, ensuring smooth operation in kiosk mode. ➡️ Why Me? I can easily handle your facial recognition project as I have 5 years of experience in computer vision, mobile development, and database integration. My skills include API design, application packaging, and user experience optimization. I also have a strong grip on technologies like TensorFlow Lite and Microsoft SQL Server. ➡️ Let's have a quick chat to discuss your project in detail and let me show you samples of my previous work. Looking forward to discussing this with you in chat. ➡️ Skills & Experience: ✅ Computer Vision ✅ Facial Recognition ✅ TensorFlow Lite ✅ Mobile Development ✅ Kiosk Mode Apps ✅ Microsoft SQL Server ✅ API Design ✅ Capacitor / Flutter / React Native ✅ Local Database Integration ✅ User Experience Design ✅ Real-Time Processing ✅ Data Security Waiting for your response! Best Regards, Zohaib

@einnovention

With vast experience in developing and deploying highly innovative solutions on Microsoft SQL Server, we at Einnovention bring the perfect blend of expertise and relatability to your unique facial recognition project. Offering fully qualified offline face recognition capability is one of our key competencies, backed by our deep understanding of embedding models such as MobileFaceNet, FaceNet, and ArcFace. This expertise empowers us to offer not just facial detection but also cosin similarity-based local face matching, which will contribute to enhanced security and accuracy for your application. Considering your project's critical requirement of running seamlessly on kiosk mode, we assure you that we have conquered the challenges faced in continuous usage scenarios in terms of memory, battery consumption, and overall performance optimization. Our focus has always been on delivering smooth user experiences with no lag or compromise on the near-real-time detection feature you seek.

@Srashtasoft

Hi there, I have read your project requirement. You need to replace cloud-based facial recognition with a fully offline, on-device solution, integrate it with an on-premise MS SQL Server, implement kiosk-mode mobile apps (iOS & Android), and ensure real-time face detection with stable continuous operation. We can develop a robust offline facial recognition system using TensorFlow Lite (MobileFaceNet/ArcFace), implement fast local matching, and design a secure offline-first architecture with sync capabilities to MS SQL via API/VPN. We will also build a stable kiosk-mode mobile app with auto-triggered face detection, optimized performance, and full deployment support for App Store and Play Store. The solution will be scalable (500–1000 users), secure, and production-ready. A few questions to clarify before proceeding: ==================================== Do you prefer to extend the existing Base44 app via native bridge (Capacitor) or migrate to Flutter/React Native for better performance? Is there an existing API layer for MS SQL Server, or should we design a secure middleware for synchronization? Do you require anti-spoofing (liveness detection) in this phase or as a future enhancement? What target devices (specific tablets/models) should we optimize performance for? Best Regards, Srashtasoft Team

@InterconnectBPO

Hi, We can deliver a fully offline, kiosk-ready facial recognition system with reliable on-device performance and secure syncing built for real-world continuous use. Our approach: • Offline recognition: TensorFlow Lite (MobileFaceNet/ArcFace) + ML Kit for detection, cosine similarity matching, optimized for <1s response • Kiosk mode: Native Android (lock task mode) + iOS (Guided Access / supervised mode), full-screen, auto-trigger detection, auto-reset flow • App layer: We recommend Flutter for stability + native plugins (or extend your current Base44 via Capacitor if preferred) • Local storage: Encrypted SQLite (embeddings + user data), offline-first architecture • MS SQL integration: Secure API layer (gateway/VPN), background sync with conflict handling What we deliver: • Android (APK/AAB) + iOS (IPA) builds • Fully working offline recognition system • Kiosk-mode configured app (tablet-ready) • Secure sync with on-prem MS SQL • Deployment to App Store & Google Play • Full documentation + walkthrough Experience: We’ve built mobile apps with embedded ML, real-time processing, and secure backend integrations, focusing on performance and stability. Timeline: ~5–7 weeks Budget: Finalized after brief technical alignment We’re ready to take ownership end-to-end and ensure a robust, production-ready solution. Regards Interconnect Team

@vishal1145

Your kiosk will fail during peak hours if the face detection pipeline blocks the main thread. I've seen this exact pattern crash three attendance systems when 50+ employees queue up during shift changes. Quick question - what's your expected concurrent user load during morning check-ins, and are you planning to run this on consumer-grade tablets or industrial Android devices? The hardware choice directly impacts which embedding model (MobileFaceNet vs ArcFace) will maintain sub-500ms recognition without thermal throttling. Here's the architectural approach: - OFFLINE FACIAL RECOGNITION: Implement TensorFlow Lite with MobileFaceNet for 128-dimensional embeddings, achieving 95%+ accuracy up to 500 users without cloud dependency. Add MTCNN for multi-face detection to handle queue scenarios. - KIOSK MODE STABILITY: Build Android MDM integration using Fully Kiosk Browser APIs and iOS Guided Access with watchdog process. Implement frame skipping (process every 3rd frame) to prevent memory leaks during 12-hour continuous operation. - MS SQL SERVER SYNC: Create a REST API middleware layer with JWT authentication to avoid exposing database ports. Implement delta sync with conflict resolution using last-write-wins strategy and local SQLite queue for offline transactions. - REAL-TIME DETECTION TRIGGER: Use ML Kit's face contour detection with confidence threshold of 0.7 to auto-trigger recognition only when face is centered and well-lit. Add liveness detection (blink detection) to prevent photo spoofing. - CAPACITOR NATIVE BRIDGE: Wrap the recognition engine as a Capacitor plugin to preserve your Base44 codebase while injecting native performance. Alternative: I can migrate to Flutter if you need cross-platform code sharing for future features. I've built similar biometric systems for 2 manufacturing clients processing 800+ daily check-ins on wall-mounted Samsung tablets. The critical failure point is always thermal management and camera autofocus lag - let's discuss your hardware specs and expected deployment environment before finalizing the embedding model.

@codefusion53

Hi I am excited to propose a solution for your Offline Facial Recognition Module and Mobile App Packaging project. With extensive experience in mobile development, computer vision, and secure database integration, I can deliver a robust kiosk application that meets all your requirements. My approach involves implementing on-device facial recognition using TensorFlow Lite with MobileFaceNet for reliable performance. The application will operate in a full-screen kiosk mode, ensuring continuous face detection and recognition without user navigation. I will establish a secure connection to your Microsoft SQL Server for data synchronization while maintaining offline functionality. I have a proven track record of developing similar applications and can provide clean, maintainable code along with thorough documentation. My proposed timeline for this project is 6 to 8 weeks, with a budget that aligns with the complexity of the tasks. I look forward to discussing this project further and demonstrating my relevant experience in facial recognition and mobile app development. Best, Justin

@rbtech1984

Hello There!!! ★★★★ (Offline facial recognition kiosk app with on-device AI + MS SQL integration) ★★★★ Project understanding: You need a fully offline facial recognition system replacing cloud API, running in kiosk mode on Android/iOS devices. It must detect faces in real time, match locally stored embeddings, integrate with on-prem MS SQL Server, and work in a secure sync + offline-first setup. Services: ⚜ On-device facial recognition (TFLite / FaceNet / ML Kit) ⚜ Kiosk mode full-screen locked app (Android & iOS) ⚜ Real-time face detection trigger system ⚜ Offline database (SQLite/Realm) with encryption ⚜ MS SQL Server secure API sync layer ⚜ Capacitor/Flutter native bridge integration ⚜ App store deployment (Android + iOS) Approach: I will implement edge AI model for embeddings + cosine matching, build kiosk locking layer, and create secure sync API for MS SQL. Focus will be on fast (<1s) recognition, stability and offline reliability. I have worked on mobile AI + computer vision based attendance systems and offline-first apps before. Let’s discuss, I can suggest best architecture for your scale. Warm Regards, Farhin B.

@blackrockwise

Hi there, I like how you have outlined your project description for an offline facial recognition system integrated with kiosk mode and on-premise database. Your project aims to replace the current cloud-dependent facial recognition with a fully offline, on-device solution, ensuring accurate and real-time user identification in a kiosk-style mobile application on both iOS and Android. The requirements for kiosk mode stability, local secure data storage, and synchronization with a Microsoft SQL Server on-premise database are critical and well-defined. With extensive experience in mobile app development, TensorFlow Lite, and on-device ML models like MobileFaceNet and ArcFace, I can deliver a highly reliable offline facial recognition module. I have previously implemented offline-first architectures and kiosk mode applications on both platforms, ensuring seamless full-screen operation and user privacy. Moreover, I am proficient in integrating secure API layers for MS SQL synchronization with conflict resolution. My approach involves developing a native plugin integrated into your existing Base44 app via Capacitor or proposing a React Native migration to optimize cross-platform support. I will ensure encrypted local storage for facial embeddings and user data, coupled with performance optimization to achieve near real-time recognition. I am excited to discuss your project further and provide a detailed timeline and demo examples from my portfolio. Please feel free to contact me to start collaborating!

@ashinnd84

Hello there, we are a team of Full Stack Developers in Web, Mobile and Data Analysis. Please, send me a message to discuss the work and finish in no time. Thanks Ashish Kumar.

@johnallenvillan1

Hello, I see you need to replace your current cloud‑based Google Vision setup with a fully offline recognition pipeline that still integrates with your existing Base44 PWA. The requirement for stable kiosk mode with continuous camera triggering is a clear indicator that you need a tightly optimized native layer. I’ve delivered offline facial recognition systems using MobileFaceNet + TensorFlow Lite, including one that handled more than 800 users with sub‑second matching and embedded cosine‑similarity search. I also built a kiosk‑mode tablet app for a manufacturing client that ran 24/7 without user escape paths. The main challenge here is combining continuous video processing with device‑level stability while maintaining clean synchronization with your on‑prem SQL Server. Poorly designed sync logic or model handling can cause drift, lag, or recognition failures once scaling past a few hundred embeddings. I’ll implement a native plugin that handles on‑device detection, embedding, encrypted storage, and kiosk lock controls, then integrate this with your Base44 app or provide a migration plan if needed. I’ll also design an offline‑first sync layer with conflict handling and API‑based secure connectivity to your SQL Server. For next steps, I can deliver a structured timeline covering model integration, kiosk mode, sync layer, and packaging on both stores. Thanks, John allen.

@devbaloni1983

Hello, I am an expert with 15+ years of experience in the technical world, delivering simple to complex websites, e-commerce platforms, membership systems, and custom portals. I always provide clear communication, continued support after delivery, and 100% client satisfaction. I specialize in PHP development, building secure, scalable, and high-performing web applications with custom scripts, API integration, and database management (MySQL, MariaDB, etc.). From dynamic websites to enterprise-level solutions, I focus on delivering clean code and business-driven results.

@itswahabahmad

Building on my strong portfolio and significant experience in mobile app development, particularly across Android, I am ready to bring your vision of an offline facial attendance with kiosk solution to life. My proficiency in Mobile App Development and PHP aligns perfectly with your project requirements of establishing robust, efficient connections, such as with your On-Premise Microsoft SQL Server database using local storage and secure connections via API layer/VPN/gateway. I have the unique advantage of being well-versed in different coding languages including Flutter and React Native should you consider any migration needs from Capacitor plugin/native bridge as mentioned. Combining this with my competent skills in SQLite/Realm/ObjectBox for secure local storage and data encryption makes me a reliable option to handle your critical project requirements. Having worked extensively on UI/UX optimization projects, ensuring smooth user-experiences without lags or downtime is a top priority for me. With practically zero-dependency architecture backed by over six years of game development experience focusing on performance, scalability, and quality using optimized gameplay systems, you can be assured that my delivery will meet - if not exceed - your expectations. So let's talk further about how I can help bring your project to fruition!

@mughiraaa

Hello, I will move the facial recognition fully to an offline on device system using tensorflow lite models such as facenet or arcface for embedding generation and a lightweight face detection model to trigger recognition only when a face is present. all matching will happen locally using cosine similarity against encrypted stored embeddings in sqlite or objectbox so the system works without any internet dependency. kiosk mode will be configured using android lock task mode and ios guided access to lock the device into full screen operation with continuous camera access and automatic reset after each detection cycle for the next user. integration with microsoft sql server will be handled through a secure api layer hosted on your on premise system so the mobile app never connects directly to the database, and offline first sync logic will queue updates locally and sync when available. the existing base44 app will be extended using a native bridge or capacitor layer so the recognition module integrates without breaking current features. i will optimize performance for real time detection on tablets, manage memory for continuous camera usage, and ensure stable long running kiosk operation. Let's have a detailed discussion, as it will help me give you a complete plan, including a timeline and estimated budget. I will share my portfolio in chat I look forward to hear from you. Thanks Best Regards, Mughira

@Hubready

Hello! I am a US-based senior software engineer with extensive experience in mobile app development and facial recognition technology. I carefully read the project description and understand your needs for an Offline Facial Recognition Module and Kiosk. With over 15 years in software engineering, I have developed numerous production-grade applications. I specialize in PHP, mobile app development for iOS and Android, as well as computer vision and embedded systems. I’m excited about the opportunity to create a robust solution that meets your requirements. Could you please clarify the following questions to help me better understand the project? 1. What specific features do you envision for the kiosk interface? 2. Are there any particular security protocols you’d like to implement for the facial recognition data? I believe a phased approach would be beneficial, starting with the facial recognition module, followed by kiosk integration, and then mobile app packaging. This will ensure a structured and efficient workflow. I have previously worked on projects involving facial recognition and mobile apps, including an internal attendance system and a mobile event check-in app. I am serious about delivering a high-quality solution tailored to your needs. Let’s connect and discuss how I can help bring your project to life! Best, James Zappi

@GoyalRidhi

With over half a decade of responsive and responsible website development under my belt, I am well-versed in optimizing the functionalities of mobile applications. My competence with numerous technologies including Android, iPhone, iPad, PHP5, and WordPress equips me to tackle complex projects like yours head-on. I understand the underlying concerns with an offline facial recognition system and can assure you that my skills, particularly in optimizing app performance and efficiency, will come into play for a full offline functionality devoid of any facility discrepancies. Moreover, throughout my career I've frequently dealt with large databases like the one your project would entail connecting. My experience using SQL server has endowed me with the necessary expertise ensuring secure synchronization with the on-premise database while monitoring conflicts that may arise. Lastly yet most importantly, I understand the significance of a smooth User Experience (UX), especially regarding your kiosk requirement. With this in mind, I leverage optimization measures to ensure no lags or time delays during detection processes that thereby translates into a near-real-time detection system - the hallmark of any good offline facial recognition app.

@NEHABHAT92

As an experienced and highly-skilled mobile app developer with a specialization in Android and iOS platforms, I have a deep understanding of the requirements presented in your project description. For over 9 years, I've been delivering high-quality IT services that aim to turn ideas like yours into reality. One of my core strengths lies in implementing reliable and effective offline functionalities for mobile apps, which aligns perfectly with your project's key requirement. Through my expertise in Android and iOS development, I can not only deliver an offline facial recognition module using powerful technologies like ML Kit Face Detection but also ensure smooth UX with no lags or delays for near real-time face detection. Moreover, my adeptness in working with on-premise databases, including Microsoft SQL Server, will enable me to effectively integrate your mobile application with your existing infrastructure. From secure connections via API layers and handling continuous usage to an offline-first architecture & synchronization capabilities, I've got it all covered. As we build our website/app thinking about easy maintenance of server and components hence took DB security to another level by adopting no direct DB exposure approach during server management that helps web site friendly to updates as well as data breaches.

@mayankinnovative

Hi, As per my understanding: You want to replace your cloud-based face recognition with a fully offline, on-device system, integrate it with your existing app, enable kiosk-mode operation, and deploy production-ready mobile apps with secure sync to your on-prem MS SQL Server. Implementation approach: I propose a Flutter-based app (for unified iOS/Android performance) with native plugins for camera and kiosk control. For recognition, I’ll use TensorFlow Lite with MobileFaceNet/ArcFace to generate embeddings, and cosine similarity for fast local matching (optimized for 500–1000 users with <1s response). Face detection will run continuously using ML Kit or a lightweight alternative. Kiosk mode will be enforced via Android Lock Task Mode and iOS Guided Access, ensuring full-screen, non-exitable operation. A local encrypted database (SQLite) will store embeddings and user data. For MS SQL integration, I’ll design a secure API layer (Node/.NET) with offline-first sync, queueing updates locally and resolving conflicts on reconnect. Integration with your Base44 app will be via Capacitor bridge or full migration if required. Deliverables include compiled apps, full source, documentation, and store deployment support. Estimated timeline: 6–8 weeks. A few quick questions: 1. What devices (tablet models) are you targeting? 2. Do you already have labeled face datasets? 3. Any preference: keep Base44 or migrate fully? 4. Is VPN/API layer already available for MS SQL access?

@quan9824

Hi, I can build your offline facial recognition kiosk system end-to-end with a strong focus on reliability, speed, and maintainability. I recommend using Flutter for a single codebase across iOS and Android, with TensorFlow Lite (MobileFaceNet or ArcFace) for on-device embeddings and ML Kit or BlazeFace for fast face detection. Recognition will use cosine similarity with a configurable threshold and be optimized for near real-time performance (<1 second), scaling to 500–1000 users locally. For kiosk mode, I’ll implement Android Lock Task Mode and iOS Guided Access/MDM, ensuring full-screen operation, no user escape, continuous camera monitoring, and auto-reset after each scan. The system will run fully offline, with encrypted local storage (SQLite/ObjectBox) for embeddings and user data. For MS SQL integration, I’ll design a secure API layer (Node/Python) with offline-first sync, allowing the app to operate without internet and sync safely when available, including conflict handling. I can integrate into your Base44 app via Capacitor or propose migration to Flutter for better performance and native control. Deliverables include full source code, Android/iOS builds, documentation, kiosk setup guide, and store deployment.