AI Model For Identical Face Detection

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Matlab, Python, and similar tools. I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@shadabkhan92

With a diverse skill set and a proven track record in developing end-to-end solutions and AI models, my team and I would be an exceptional choice for your project. We have extensive experience in the software development domain, including proficiency in Python (with modules like OpenCV, dlib, TensorFlow, PyTorch), and an understanding of various backend frameworks which will allow us to develop a user-friendly application with fast response times. Our expertise in Machine Learning and Face Recognition adds an edge to our services. A solid understanding of these domains garnered through numerous projects will enable us to deliver a pre-trained model matching all your requirements, along with source code and comprehensive documentation that ensure easy maintainability. This model could be based on widely used frameworks such as ResNet, MobileNet or Vision Transformer as per your preference. Furthermore, we have hands-on experience working with similar deep learning models for real-time applications, guaranteeing high efficiency without sacrificing quality. Our past projects showcased our ability to balance low latency against desired precision and recall rates. Additionally, our proactive approach towards using cloud-based deployment platforms like AWS or Azure will help in ensuring a smooth integration of the API module into your existing system. In short, we offer expertise, reliability, and a commitment to excellence; all factors that make us the best fit for this project.

@amirrmusavifr

Hi, I’m an AI expert with professional experience in computer vision, with a proven track record of working on complex image processing and AI/ML model development. With skill sets: • Algorithm Development: Strong understanding of computer vision algorithms and techniques, including convolutional neural networks (CNNs), object detection, image segmentation and feature extraction. • Model Training & fine-tuning: Develop and train machine learning models tailored for image analysis and visual data interpretation. I have worked on some well-known models like YOLO, RCNN, U-Net, Deeplab, ViT etc. • AI Integration: Implement and integrate AI models into existing software and hardware systems, ensuring high performance and scalability. • Data Analysis: Analyze and process large datasets of images and video feeds to identify patterns, trends, and insights. • Data Handling: Experience in handling and processing large datasets, including image and video data. Familiarity with data augmentation techniques and synthetic data generation. • Performance Optimization: Optimize algorithms and models for real-time processing and ensure they can handle large-scale data efficiently. • Programming Skills: Proficient in programming languages such as Python. Experience with deep learning frameworks like TensorFlow, PyTorch, or Keras. • Tools & Libraries: Proficiency with OpenCV, scikit-image, and other relevant libraries. Experience with version control systems like Git.

@chiragardeshna

Hi there, I am a strong fit for this project because I have built production-ready face recognition pipelines combining detection, embedding generation, and fast similarity search with GPU optimization. I have implemented systems using PyTorch with RetinaFace or MTCNN for detection, ArcFace/FaceNet-style embeddings, FAISS for high-speed vector search, and REST endpoints for real-time inference across video and batch workflows. I would structure this with a modular PyTorch pipeline, pretrained ArcFace backbone, FAISS index for ≤100 ms search on T4, CPU fallback mode, and a demo app supporting webcam inference, batch folder search, and a REST API. I reduce risk by benchmarking precision and recall on LFW or similar datasets early, optimizing inference with mixed precision where appropriate, documenting all open-source licenses clearly, and providing reproducible training scripts for further fine-tuning. I am ready to begin immediately and can outline a phased plan for baseline model validation, performance tuning, and deployment packaging once hardware constraints are confirmed. Regards Chirag

@devanshkhandekar

Hi ,I’m an Applied ML Engineer in computer vision and have built production face pipelines that work across CCTV, mobile uploads GPU + CPU fallbacks. My Approach Face detection + tracking: RetinaFace / SCRFD with optional DeepSORT for video to reduce redundant re-embeds and stabilize IDs. Output tight bboxes + landmarks. Alignment + embeddings: landmark-based alignment -> ArcFace/AdaFace-style embedding backbone (ONNX/PyTorch). Consistent preprocessing for CCTV (low light/compression) vs mobile (wide pose). Gallery matching at scale: store embeddings + metadata; cosine similarity with thresholding + top-K retrieval. For large archives, use FAISS (IVF/HNSW) to hit <100ms/face on T4. Training/fine-tuning pipeline: dataset loader + augmentation (blur, low-light, occlusion), metric learning (ArcFace loss), validation on LFW-like splits, ROC/TPR@FPR reporting. Deployment: single-GPU low latency (batched inference) + CPU-only mode (quantized ONNX ). Dockerized Ubuntu / Py3.10. Demo + API Notebook + demo app: webcam/CCTV stream, batch folder search, and RESTreturning bboxes + similarity scores + matched IDs. Relevant experience Replaced managed face matching with a custom FaceNet/ArcFace pipeline, stored embeddings in Postgres/FAISS, and used cosine similarity + thresholds for identity resolution at scale. Built multi-environment CV services (edge + cloud) with ONNX acceleration, batching, and robust monitoring.

@phpxpert89

Hello , I checked your project, and it looks interesting. This is something we already work on, so the requirements are clear from the start. We mainly work on Python, Algorithm, Machine Learning (ML), C++ Programming, Face Recognition, Keras, Computer Vision, Deep Learning We focus on making things simple, reliable, and actually useful in real life not overcomplicated stuff. Let’s connect in chat and see if we’re a good fit for this. Best Regards, Ali nawaz

@Hemantt841

With 7 years of experience in the field, I am confident that I am the best fit to complete this AI model for identical face detection project. I have the relevant skills to deliver a high-quality solution that meets all your requirements. How I will complete this project: - Utilize my expertise in deep learning to develop an end-to-end model for identifying identical human faces across images and video in real-time. - Implement a robust system to detect human faces, draw accurate bounding boxes, and compare them against a gallery for identical matches. - Ensure compatibility with security-grade CCTV feeds, social-media style mobile uploads, and large photo-management archives. - Optimize the model for low latency on a single modern GPU and CPU-only hardware for lightweight deployments. Tech stack I will use: - Framework: PyTorch or TensorFlow/Keras - Pre-trained backbone: ResNet, MobileNet, or Vision Transformer - Libraries: MTCNN, FaceNet, dlib (with license documentation) - Environment: Ubuntu 20.04 with Python 3.10 I have previously worked on similar solutions and will leverage my experience to deliver the following: - Source code with clear, commented modules for detection, embedding generation, and similarity matching. - Pre-trained weights for immediate inference. - Demo app showcasing live webcam/CCTV inference, batch search across photos, and a simple REST or gRPC en

@mariamz24

Hi, We would like to grab this opportunity and will work till you get 100% satisfied with our work. We are an expert team which have many years of experience on Python, Algorithm, Machine Learning (ML), C++ Programming, Face Recognition, Keras, Computer Vision, Deep Learning Lets connect in chat so that We discuss further. Regards

@keaganmini

I am an excellent fit for your project, having successfully completed similar work in the past. Your need for a clean, professional, user-friendly face detection system that runs seamlessly across multiple environments while delivering automated, low-latency results is clear. I specialize in building integrated deep-learning models using PyTorch and TensorFlow, focusing on detection, embedding, and similarity matching with pre-trained backbones like ResNet and MobileNet. Even though I am new here, I have worked on numerous projects outside of freelancer and developed the skills necessary to complete this work effectively. I’d be glad to discuss your project—at best, we find a strong fit to work together; at minimum, you receive a complimentary consultation. Regards, Keagan.

@sanketpatil51515

Hi, I’m Sanket, an AI & Computer Vision developer experienced in building real-time face detection and recognition systems optimized for GPU and edge deployments. I can deliver a complete end-to-end pipeline that: • Detects all faces in images/video streams (CCTV, webcam, uploads) • Generates high-quality embeddings • Matches against a gallery with similarity scoring • Works across live stream, batch search, and API environments • Runs optimized on GPU (T4 class) and reasonably on CPU Proposed Architecture Face Detection: RetinaFace or MTCNN (MIT license) Embedding Model: ArcFace (ResNet backbone) or FaceNet (Apache 2.0) Similarity Matching: Cosine similarity + FAISS for fast search Framework: PyTorch (preferred for flexibility & performance tuning) Acceleration: ONNX + TensorRT for GPU optimization Deliverables ✔ Modular, well-commented source code ✔ Pre-trained weights ✔ Training pipeline (data loading, augmentation, fine-tuning) ✔ Demo notebook + live webcam inference script ✔ REST API (FastAPI) returning bounding boxes + similarity score ✔ Setup guide (Ubuntu 20.04, Python 3.10) ✔ License list for all OSS components Performance Targets ≥95% precision / ≥90% recall (LFW benchmark) ≤100ms per face on NVIDIA T4 Optimized CPU fallback mode I’ve worked on real-time CV systems and understand latency–accuracy tradeoffs in surveillance and large-scale search environments. Ready to start immediately and discuss dataset & deployment specifics.

@lovishjaiswal12

Hi, this is exactly the kind of project where a solid architecture upfront saves months later. I’ve spent 5+ years working with deep learning and computer vision, including face detection and recognition pipelines, and I focus on systems that are both accurate and deployable in the real world. I can build you an end-to-end pipeline with a strong detector (e.g., RetinaFace/MTCNN), a high-quality embedding model (ArcFace/FaceNet-style on ResNet or ViT), and fast similarity search, optimized for GPU but still practical on CPU. You’ll get clean modular code, training and fine-tuning scripts, ready weights, and a demo covering live video, batch search, and an API endpoint. I also document licenses and setup clearly so you can extend and stay compliant. If you want a system that’s not just a demo but a solid foundation for production, I’d love to work on this with you.

@mkassab934

Hello, I can build your end-to-end real-time face recognition system covering detection, embedding, and similarity matching across CCTV streams, mobile uploads, and large photo archives using PyTorch. The solution will be modular, optimized, and fully reproducible. What I will deliver within the project scope: • Face detection using a proven model (RetinaFace/MTCNN/YOLO-face based on speed vs accuracy) • Embedding model built on a pre-trained backbone (ArcFace/ResNet/MobileNet) with full fine-tuning pipeline • Similarity matching with cosine distance + threshold tuning • GPU-optimized inference plus CPU fallback mode Deliverables: • Clean, commented source code split into detection, embeddings, and matching modules • Pretrained weights ready for inference • Demo notebook/app for live webcam/CCTV + batch photo search • Simple REST API endpoint returning boxes and similarity scores • Training + retraining pipeline • Ubuntu 20.04 / Python 3.10 ready setup guide • List of all open-source libraries and licenses I will benchmark on LFW (or similar) and tune to reach your precision/recall and latency targets. Ready to start immediately. Best regards

@viglundurm

Hi — I can deliver an end-to-end real-time face search system (detect → embed → match) with a full training + inference pipeline and clean API. Approach: • Detection: a modern face detector optimized for CCTV/mobile (e.g., RetinaFace/YOLO-face) with accurate boxes + landmarks. • Embeddings: ArcFace-style model (ResNet/MobileNet backbone) for strong identity separation; training + fine-tuning scripts included. • Matching: FAISS (cosine/IP) for fast gallery search + thresholding; supports 1:N and batch folder search. • Deployment: PyTorch with ONNX/TensorRT optional for NVIDIA T4; CPU fallback via ONNXRuntime + quantization. Deliverables: 1. Modular source code (detector, embedder, matcher) + configs 2. Pretrained weights + export (TorchScript/ONNX) 3. Demo: live webcam/CCTV, folder search, REST (FastAPI) or gRPC endpoint returning boxes + similarity 4. Setup guide + concise API docs + license list for all OSS components I’ve built GPU-optimized inference services (FastAPI/gRPC), model export pipelines, and vector search (FAISS) for low-latency retrieval. Best regards, Viglundur

@muskanuj1910

I am an AI/ML Engineer specializing in Agentic AI systems, Large Language Models (LLMs), and predictive modeling. Currently working as a Trainee Software Engineer at GlobalLogic, I design and deploy AI-powered agent systems using Python, FastAPI, LangChain, and RAG pipelines to build scalable, intelligent, and automation-driven solutions. My expertise lies in building multi-LLM orchestration systems, optimizing retrieval pipelines, and developing AI models that improve decision accuracy and operational efficiency. I have also worked on AI-based battery management system prediction models at NIT Delhi, where I implemented Neural Networks and LSTM architectures to enhance forecasting performance and model stability. I am passionate about solving real-world problems using data-driven approaches, building reliable AI systems, and continuously exploring advancements in Generative AI and intelligent automation.

@shawaizz1

Dear I am submitting my project on Real-Time Face Recognition with Multi-Environment Support. The system implements an end-to-end deep learning pipeline capable of detecting, embedding, and matching human faces across images, video streams, and large photo archives. Key Highlights: Face Detection: Detects all human faces in images or live streams with accurate bounding boxes. Face Embedding & Recognition: Uses a ResNet-based ArcFace model to convert faces into embeddings and match against a gallery using cosine similarity. Similarity Search: Employs Faiss for fast and scalable face search, supporting large galleries. Deployment: Includes a live webcam/CCTV demo, batch photo search, and a REST API endpoint for integration. Performance: Achieves ≥ 95% precision and ≥ 90% recall on standard datasets, with ≤ 100 ms inference per face on NVIDIA T4 GPU. Fully compatible with CPU-only deployment for lightweight use. The project is modular, with separate code for detection, embedding generation, and similarity matching, making it easy to extend and train further. All code runs out-of-the-box on Ubuntu 20.04 with Python 3.10. Please find the source code, pre-trained weights, and demo notebook included for evaluation. Thank you for your time and consideration.

@hassam945

Hi there, its daily work you see my profile-portfolio section that aligns and I can build your real-time face detection + identical face matching pipeline end-to-end using a proven stack (RetinaFace/MTCNN + FaceNet/ArcFace embeddings + cosine similarity) optimized for GPU with CPU fallback. I’ll deliver modular, well-documented code, training pipeline, pretrained weights, and a demo covering live stream, batch search, and REST API. I have worked on AI vision systems like workout posture and behavior analytics, so accuracy, latency, and reproducibility will be handled from day one. "I prefer to discuss first because we both need to understand first each other then if its align we start working . So its not a complex for me only the thing I observe your work need clarity in Professional manner .So you will evaluate me with any task before awarded if you like my work in professional manner then you will now hire me. Last thing , I am not here for juggling clients you visit my freelancer profile it gives you clarity about all things you need .I am confident about that thing I deliver your work with requirement satisfaction and Clarity".

@mike3608

I understand your need for a robust deep-learning model capable of identifying identical human faces in various media environments. With extensive experience in both TensorFlow and PyTorch, I am confident in building a high-precision model using a pre-trained backbone like ResNet for your needs. My approach includes crafting well-documented source code for detection, embedding generation, and matching, ensuring compatibility across GPU and CPU setups. I will also provide a comprehensive setup guide, API documentation, and a demo application to meet your specified acceptance criteria and ensure ease of use across your required platforms. Let's achieve exceptional real-time face recognition performance together!

@InjectAI

Hello, I full understood your requirements. I already Developed such type of systems. Pleas Visit my Profile to see related Projects. what I will Deliver:- Modular Python code for face detection, embedding generation, and similarity matching Pre-trained backbone (ResNet / MobileNet / ViT) with full training pipeline for future improvements Demo • Live webcam / CCTV inference • Batch search across folders of photos • REST or gRPC endpoint returning face locations and similarity scores Pre-trained weights ready for inference Clear setup guide and API documentation List of open-source libraries used with licenses (e.g., MTCNN, FaceNet, dlib) for compliance Key Features: ≥ 95% precision / ≥ 90% recall on LFW or comparable dataset Mean search time ≤ 100 ms per face on NVIDIA T4 Runs out-of-the-box on Ubuntu 20.04 with Python 3.10 Low-latency performance suitable for CCTV, mobile uploads, and photo archives I am offering yout introductory rate. because I am building my Profile Lets open chat for further discussion Regards: Abdul Salam AI Computer Vision Developer

@nikamvaishnavi23

I have done my engineering in AIML and have hands on experience in training machine learning projects , I have worked on several projects like drivers drowsiness detector which can help me to this project as I already have the understanding of detecting human face using an ML model