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I will computer vision object detection, face recognition and opencv python app
Build Smart Vision AI That Works in Real Time
I'm john, a Computer Vision and AI specialist with strong expertise in building intelligent systems that help businesses automate image and video analysis. I develop solutions such as object detection...
Sobre este Serviço
Need a face recognition system or a custom OpenCV Python application that actually works in real-world conditions? Generic tutorials won't cut it for your project.
I build production-ready computer vision apps with face recognition, object detection, and OpenCV for security systems, attendance tracking, and smart access control.
SERVICES I PROVIDE:
Real-time face detection and recognition system
Multi-face tracking with unique identity labeling
Attendance system with automated CSV or database logging
Emotion and age detection using deep learning models
Object detection integrated with face recognition
OpenCV-based Python GUI desktop application
API-ready deployment for web or mobile integration
WHY CHOOSE ME:
Delivered 50+ working CV applications to global clients
Code runs on both CPU and GPU environments
Full source code, documentation, and setup guide included
Ongoing support until your system runs perfectly
I work with security firms, HR tech platforms, schools, retail stores, and IoT hardware projects requiring real-time vision intelligence.
Send me your use case and I will recommend the right approach before you order.
Outros serviços de Ciência de dados e ML que eu ofereço
Perguntas frequentes
How many faces can your system recognize simultaneously in a live video feed?
The system can recognize multiple faces simultaneously — typically 10 to 20 faces in a single frame on a mid-range GPU. Performance depends on your hardware. I optimize the detection pipeline so that recognition speed stays above 15 FPS even on systems without a dedicated GPU, using face embedding.
Can you build this system so it works without an internet connection?
Yes. The entire system runs fully offline on your local machine. No API calls to cloud services are required. The face recognition models are embedded and run locally using libraries like DeepFace, dlib, or InsightFace. This is ideal for private security environments where data privacy is critical.
What happens when the system encounters a face it has never seen before?
Unknown faces are flagged as "Unrecognized" and optionally saved as a new entry for review. I can also configure alert behavior — such as triggering a sound, logging a screenshot, or sending an email notification — whenever an unrecognized face appears in the feed.
