Custom Face Recognition System
A custom AI face recognition system that classifies known faces, detects users in real time, and supports dynamic face addition through webcam input.
Face recognition systems are useful when users need to identify people quickly from images or live camera input. However, many systems are difficult to customize when new faces need to be added or when the model needs to improve with new data.
This project solves that problem by building a custom face recognition system using EfficientNet B0 with TensorFlow. The system can classify known faces, detect faces through a real-time webcam feed, and recognize users based on trained face data.
It also supports dynamic face addition, allowing users to capture and store new faces during webcam sessions. With separate scripts for training, prediction, and real-time detection, the system provides a flexible workflow for custom face recognition and continuous model improvement.
Key benefits
- Recognizes faces in real time using webcam input
- Uses EfficientNet B0 for accurate and efficient classification
- Allows users to add new faces dynamically
- Supports custom dataset training and prediction
- Helps classify unknown faces and improve performance over time
What's included
- EfficientNet-based face classification model
- Real-time webcam face recognition system
- Dynamic face capture and storage feature
- Training and prediction scripts
- Unknown face classification workflow
Use Cases
- Real-time identity recognition
- Custom face classification system
- Webcam-based access support
- Attendance or user verification system
- Unknown face detection and labeling
Key deliverables
- Custom face recognition model
- EfficientNet B0 training pipeline
- Real-time webcam detection system
- Dynamic face addition feature
- Face prediction and classification scripts
- OpenCV-based recognition workflow