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English
$
USD
I will build face recognition and face detection system using opencv
About this Gig
Need a face recognition, face detection, or emotion detection system built fast and accurately?
I build secure, real-time facial AI systems using OpenCV, MediaPipe, DeepFace, and deep learning for businesses, schools, and developers.
What I offer:
- Fa'ce detection in images, videos, and live camera feeds
- Fa'ce recognition for attendance and access control systems
- Emotion detection (happy, sad, angry, neutral, surprised)
- Hand gesture recognition using MediaPipe Fingerprint and biometric system integration Multi-face tracking in crowded environments
- GUI desktop app or REST API (Flask or FastAPI)
- Database integration (SQLite, MySQL, Firebase)
Why choose me:
- Systems built to 95%+ accuracy on real-world environments
- Works on standard webcams, IP cameras, and CCTV setups
- Fully documented Python code delivered every time
- Tested under low-light and varied angle conditions
- Repeat-client rate above 70% buyers come back
Drop me a message before ordering to discuss your exact setup.
FAQ
Can the face recognition system identify people even when they are wearing masks or glasses?
Yes. I train the model with augmented data that includes occlusions such as masks, glasses, and partial face visibility, improving recognition accuracy in real-world security environments.
Can this system run locally without sending any face data to a cloud server?
Absolutely. I build fully offline, on-device systems using OpenCV and local databases so no biometric data ever leaves your hardware, important for GDPR and data privacy compliance.
How many faces can the database store, and how fast does recognition happen in real time?
The system scales to thousands of registered faces. Real-time recognition typically processes 20–30 frames per second on a standard CPU, and faster on a GPU or NVIDIA Jetson device.
