đź“·Â Facial Recognition System
đź“·Â Facial Recognition System
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Harr Cascade
Harr Cascade
Overview:
A deep learning–based Facial Recognition System built with ResNet50, capable of identifying celebrities from the LFW (Labelled Faces in the Wild) dataset. The system leverages transfer learning for efficient model training and integrates real-time face detection using OpenCV.
Overview:
A deep learning–based Facial Recognition System built with ResNet50, capable of identifying celebrities from the LFW (Labelled Faces in the Wild) dataset. The system leverages transfer learning for efficient model training and integrates real-time face detection using OpenCV.
Key Features:
Key Features:
Deep Learning Model (ResNet50): Transfer learning for accurate face classification.
 Training Pipeline: Custom training using PyTorch on the LFW dataset.
 Real-Time Recognition: Face detection and recognition through a webcam feed powered by OpenCV.
 GPU Acceleration: Supports CUDA for faster training and inference.
Technologies Used:
Technologies Used:
Python, PyTorch, OpenCV
ResNet50 (pretrained model)
TQDM for training progress
Future Enhancements:
Future Enhancements:
Add FPS counter for real-time performance monitoring.
 Expand dataset for improved generalization.
 Integrate face embeddings for advanced recognition capabilities.
Impact:
This project demonstrates the application of deep learning and computer vision to real-world tasks, combining efficiency, accuracy, and real-time usability.
For Source Code:
David Wells
David Wells
Igor Ivanov
Igor Ivanov
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