Mobile Face Verification

@onkarkulkarni8

Hi, I can help with your "Mobile Face Verification" project. I develop clean, maintainable PHP backends — core PHP and Laravel/Symfony, MySQL schema design, and well-structured REST APIs. For work involving php, mobile app development, android, software architecture, machine learning (ml), face recognition, artificial intelligence, kotlin, flutter, computer vision, I pay close attention to validation, security, and readable code, delivering in small, testable milestones. I provide clean commits and clear documentation. Can we discuss the specifics before I firm up the timeline? ⭐ 5.0/5 from a recent client: "Very Professional and On time delivery of the project" Final timeline and cost will be confirmed in chat after a complete understanding and documentation of the project expectations in detail.

@maryam951

I am confident that I have the skills and experience to provide you with an impeccable mobile face verification solution. My extensive knowledge in Flutter and PHP allows me to create high-performing, cross-platform applications - essential for this project's need to run on both iOS and Android. My expertise in TensorFlow model deployment will ensure your project not only performs swiftly but also complies with your preferred stack. Moreover, with my profound understanding of automation and system integration, I can guarantee the lightweight solution we build won't burden your field workers' devices while maintaining its reliability. As a bonus, my proficiency in CRM systems will help streamline the process of syncing your local logs to the server once connectivity is regained. Lastly, the fact that I've helped businesses optimize their workflows for over four years means that I possess the finesse required to architect clean and coherent systems alongside well-documented source codes. By opting for my services, you're not only investing in a top-tier prototype APK but also ensuring a smooth handover post-completion, as I enable your internal team to understand and maintain the system with ease. Choose me, and let's build a solution that not only meets your requirements but exceeds your expectations!

@srdank001

Hello, How are you? I already have a mobile app with facial recognition capabilities that meets your requirements. I developed it a few months ago. If you are interested, let's discuss the details and move forward. Thanks Srdan

@Geetam00

Hi, I'm Geetam from Rajasthan, India, with 5+ years of industrial experience across different technologies and software stacks. I carefully reviewed your requirements, and yes, I can deliver this project within your expected timeline. If you're ready, let's schedule a quick meeting and get started. As a top-tier developer who thrives on building innovative and impactful solutions, I assure you that your mobile face verification project is in proficient hands with me. My five years of experience in full-stack development has provided me with the necessary skills and knowledge to create exactly what you’re seeking for your workers. Speaking to your key expectations, I propose the utilization of Kotlin paired with TensorFlow-Lite. Not only will this guarantee optimal performance, but it also aligns with your preferred stack. For hardware, I'll employ mid-range Android devices that are commonly used by field workers, ensuring real-world testing scenarios for better benchmarking. Regarding FAR/FRR and offline capability, our team has a proven track record of achieving remarkable results. While the actual performance depends on the dataset and model fine-tuning, we can obtain an FAR/FRR ratio comparable to commercial SDKs. As for offline capacity, our extensive understanding of optimizing edge-optimized libraries like TensorFlow-Lite ensures a fast and seamless experience for your users.

@itzdikshantgupta

Hello, Your requirement for a **lightweight face-verification solution** that runs **entirely on the mobile phone with no dependency on a data connection** is clear. The focus on fast offline verification, encrypted biometric templates, and maintainable source code aligns well with a robust on-device AI approach. Here's how I can help: ✔ Build the solution using **Kotlin or Flutter** with **TensorFlow Lite** (or a better edge-optimized library if benchmarking shows higher accuracy). ✔ Store biometric templates securely using encrypted local storage. ✔ Deliver verification in under two seconds on mid-range Android devices with local logging for later server sync. ✔ Write clean, well-commented code that's easy for your internal team to maintain. ✔ Provide a prototype APK first, followed by complete source code and documentation. Regarding your questions: ✔ Proposed model: TensorFlow Lite with an optimized face embedding model suitable for offline verification. ✔ Performance benchmark: Mid-range Android devices (Snapdragon 7-series or equivalent) for realistic testing. ✔ FAR/FRR: The exact rates depend on your dataset and threshold tuning, and I'll work with you to optimize them for your use case. Clear communication, regular updates, and thorough testing will ensure a reliable solution before delivery. Let's discuss your onboarding flow and verification requirements so I can begin building the prototype APK right away. Best regards, Dikshant

@abhir131

the client know that you have thoroughly read the project details and understood what is being required. Make sure that your proposal is relevant to the tasks specified on the project description. Explain your time frame and

@puneetsivananda

I have understanding of your requirement would like to help you create this POC, i would propose MobileFaceNet, hardware is POCO c3 mobile phone

@srijank00

I can deliver a working prototype within 10 hours. If you're happy with the result, you can pay me. If it doesn't meet your expectations, you don't have to pay anything. I've worked on AI-powered mobile applications and can build a lightweight offline face verification solution that is fast, secure, and easy to maintain. I'll provide clean source code and keep you updated throughout the development. Looking forward to working with you!

@UmairBuildsAI

Hey, Flutter and TensorFlow Lite are within my stack and I can build this offline face verification system for your field workers. You can review my work here: https://www.freelancer.pk/u/UmairBuildsAI My plan: Flutter app using TFLite with a MobileFaceNet or similar edge-optimized face embedding model for on-device verification, no internet required post-installation. Enrolment flow captures and stores the reference embedding locally with AES encryption at rest, live selfie compared against stored template on each app open with a clear verified/not verified result in under two seconds on mid-range Android. Local log of verification outcomes with timestamp, synced to your server when connectivity is restored. Clean, well-commented source with a prototype APK as the first milestone. For the model, I'd propose MobileFaceNet via TFLite for its balance of speed and accuracy on mid-range hardware, typically achieving FAR/FRR comparable to commercial SDKs at under 1% in controlled lighting. What device will you use for the performance benchmark? Happy to jump on a quick call to confirm the enrolment flow details. Best Regards, Umair

@arav38vilayatkar

Hi there, I hope this message finds you well. I noticed your interest in a Mobile Face Verification project and believe my background makes me an ideal candidate. With over 9 years of IT industry experience, I've successfully completed numerous projects involving PHP, Mobile App Development, Android, Software Architecture, Machine Learning, Face Recognition, AI, Kotlin, Flutter, and Computer Vision. My portfolio showcases solutions for similar challenges. For this project, I propose leveraging my expertise in developing robust face verification systems tailored to mobile platforms. My approach includes designing scalable architectures, integrating advanced ML techniques, and ensuring seamless integration across Android and potentially other platforms. My goal is to deliver a highly secure, efficient, and user-friendly solution that meets all your requirements within your budget range. Let’s discuss how we can turn this vision into reality. Looking forward to our conversation. Best, [Your Name]

@ffatihgur

Hi, I have 5 years of experience in software engineering, heavily focusing on Artificial Intelligence, computer vision, and edge computing. I can build this lightweight, offline face-verification solution for your field workers. Since this is going to be my very first project on Freelancer, I am extremely excited and highly motivated to deliver perfect results to earn a 5-star review. I would be very grateful if you could give me a chance to prove my skills. To answer your specific questions: Model: I propose using MobileFaceNet or BlazeFace via TensorFlow-Lite. They are highly optimized for edge devices and perform excellently without internet. Hardware: I will run performance benchmarks on a mid-range Android device (e.g., Snapdragon 6 or 7 series, like Samsung Galaxy A-series) to ensure the verification happens in under 2 seconds. Typical FAR/FRR: In offline mode, we can achieve a False Accept Rate (FAR) of < 0.1% and a False Reject Rate (FRR) of around 1-2%. We can fine-tune this threshold together based on your strictness level. I can develop the app using Flutter/Kotlin, ensuring all local biometric templates are securely encrypted. I am ready to start working on the quick prototype APK immediately. Looking forward to discussing the details with you!

@CliveMSoftware

I'll build this using TensorFlow Lite with a lightweight face embedding model (MobileNetV2 or similar) paired with Kotlin for Android, handling enrollment capture, encrypted local storage of face templates, and real-time verification in under 2 seconds on mid-range devices. The workflow matches your spec exactly: reference image captured at onboarding, stored encrypted with Android Keystore, then live selfie comparison on app open with pass/fail logic plus local logging. I'll benchmark on a Snapdragon 665 class device and target FAR/FRR in the 1-3% range depending on your threshold tuning, which we can adjust together. Deliverables include a working APK prototype, complete Kotlin source code, encryption implementation details, and docs covering model choice, performance metrics, and maintenance notes for your team.

@sakets16

The system relies on a three-stage pipeline optimized for mobile CPUs/NPUs. Instead of storing actual images, the system extracts mathematically compressed "embeddings" (biometric vectors) during enrollment and performs microsecond-fast comparative math locally during verification.

@sumayanazir440

Hi, I can build an offline-first face verification app that performs fast, secure on-device authentication for your field workers. **Proposed solution:** * Flutter with TensorFlow Lite (or MediaPipe, depending on performance). * Offline face verification after enrollment. * Encrypted storage for biometric templates. * Local verification logs with automatic sync when connectivity is restored. * Optimized for verification in under 2 seconds on mid-range Android devices. **Answers to your questions:** 1. **Model/Library:** TensorFlow Lite with an edge-optimized face embedding model (MediaPipe is also an option). 2. **Benchmark Hardware:** Mid-range Android device (or your target device if provided). 3. **FAR/FRR:** This depends on enrollment quality and the selected model. During the prototype, we'll tune the threshold to achieve the desired balance between security and usability. **Deliverables:** * Prototype APK * Full source code * Documentation and setup guide * Clean, maintainable code I estimate **5–7 days** for the prototype and **10–14 days** for the complete solution. I look forward to discussing your requirements. Best regards, Sumaya

@EnriqueHR

Give me one minute and I’ll show you exactly how I’d deliver this. I've gone through your job post and have a good understanding of what you're looking to achieve. I'd love to help you bring this to life and deliver something that truly meets your needs while making the entire process smooth and stress-free. Our recent success in a similar project showcases our expertise in achieving positive outcomes. I care about getting things right and making the process simple, reliable, and aligned with your goals from start to finish. Your project requires a lightweight face-verification solution for field workers that runs offline. I propose utilizing TensorFlow-Lite for fast verification and encrypted local storage for biometric templates. With my experience in developing efficient mobile solutions, I'm confident in delivering a seamless and secure face-verification system tailored to your specific needs. I'd love to chat about your project and see if we're a good fit. Feel free to reach out for a free consultation for valuable insights. Regards, Enrique.

@AkshayKhairnar01

Hello, I can build this as a fully offline Flutter-based face verification solution for Android. I have experience integrating Flutter with TensorFlow Lite, camera processing, and on-device ML. **Proposed stack:** * Flutter + TensorFlow Lite * MediaPipe Face Detection * MobileFaceNet/FaceNet TFLite for face embeddings * Android Keystore + encrypted local storage for biometric templates The solution works entirely offline after enrollment. **Performance target:** * Tested on mid-range Android devices (Snapdragon 695/778G or equivalent) * Verification in under 2 seconds (typically around 1 second) **Expected accuracy:** With proper enrollment and threshold tuning: * FAR: ~0.1–1% * FRR: ~1–3% **Features:** * Offline face enrollment * Live selfie verification * Secure encrypted template storage * Verified/Not Verified result * Local verification logs for later sync * Clean, modular, and well-documented source code **Milestones:** 1. Prototype APK with offline enrollment and verification. 2. Full source code, encryption, logging, documentation, and optimization. The current listed budget is quite low for the scope of building a secure, production-quality offline face verification system. If you're open to discussing a realistic budget, I'd be happy to deliver a robust and maintainable solution. I look forward to discussing your requirements.

@prachia77

Hi, Your requirement for an offline face-verification solution is clear, and this is the kind of mobile workflow that needs both speed and reliability on the device side. I have experience with Android and Flutter development, AI-based mobile features, and building lightweight app flows that prioritize performance, security, and maintainability. The solution would include onboarding image capture, encrypted local template storage, live selfie verification, and a local verification log for later server sync. For the recognition pipeline, TensorFlow Lite or another edge-optimized face recognition library would be a practical choice depending on the accuracy and speed targets you want to achieve on mid-range Android devices. My focus would be on keeping verification fast, fully offline after enrolment, and simple for field workers to use. A prototype APK can be delivered first for testing, followed by the complete source code and brief documentation for your internal team. Best regards, Prachi Agrawal

@erdavinder2307

Hi there, I saw your post for mobile face verification. Given the budget, this looks like a targeted MVP feature or a specific integration within an existing application. UNDERSTANDING YOUR REQUIREMENTS Integrating face authentication capabilities into a mobile app. Managing camera access and routing to native security or a matching service. EXPECTED CHALLENGES & SOLUTIONS Scope clarity between unlock and identity verification. Local device biometrics are fast but only prove the device owner is present. Comparing a live selfie against an ID requires external AI. I will help route this to local SDKs or a low-cost API like AWS Rekognition based on your actual goal. Lighting and device variability. Mobile cameras perform differently under varying conditions. I will ensure the UI guides the user clearly and handles permission states gracefully to avoid crashes when camera access is denied. SKILLS & EXPERIENCE 13+ years of software development running Solidev Electrosoft. Extensive background in Flutter and mobile integrations, including camera modules and cloud AI services. Delivered mobile AI integrations like the Mindset Fuel app. QUESTIONS Are you looking for native device unlock or a selfie-to-ID comparison? Is this for an existing app, and what framework is it built on? Given the budget, are we focusing on a minimal proof of concept? I am ready to act as your technical partner to get this built efficiently. Best regards, Davinder

@infobytest

Hi there, I can deliver a lightweight, 100% offline face-verification solution for your field workers using Flutter/Kotlin paired with an edge-optimized AI model. Answers to Your Critical Questions: Proposed Library/Model: I propose using Google ML Kit Face Detection (to extract bounding boxes and landmarks) combined with an optimized MobileFaceNet or FaceNet (TFLite quantized) model to generate 128-D/512-D face embeddings. Real-time comparison will be done locally via Cosine Similarity or Euclidean Distance. Benchmark Hardware: I will test and benchmark on mid-range devices like the Samsung Galaxy M series or Redmi Note series (typically running 4GB–6GB RAM) to guarantee execution under 1.5 seconds. Target FAR/FRR: With proper threshold fine-tuning, we can achieve a False Acceptance Rate (FAR) of ~0.01% and a False Rejection Rate (FRR) of ~1-2%, matching standard commercial offline metrics. Our Technical Execution Plan: Security: Local face templates will be encrypted at rest using AES-256 via Flutter Secure Storage / SQLCipher. Offline Logs: Verified events will be stored in a local SQLite/Isar database, auto-syncing to your server using a background sync manager once internet connectivity is detected. Looking forward to collaborating! Best regards,