AI Face Detection and Recognition System This project develops an AI application that can detect and recognize faces in images or videos. It uses pre-trained face detection models like Haar cascades or deep learning-based face detectors. Additionally, face recognition capabilities are implemented using techniques such as Siamese networks or ArcFace.

Introduction This project aims to create a robust face detection and recognition system. By leveraging pre-trained models and advanced face recognition techniques, the system can accurately detect and identify faces in various images and video streams.

Features Face Detection: Uses pre-trained Haar cascades or deep learning models for face detection. Face Recognition: Implements face recognition using techniques like Siamese networks or ArcFace. Real-time Processing: Supports real-time face detection and recognition in video streams. Scalable: Easily extendable to recognize a large number of faces.

Prerequisites Python 3.7+ OpenCV TensorFlow or PyTorch NumPy Scikit-learn Dlib (optional for additional face detection

Model Architecture Face Detection Haar Cascades: Utilizes Haar cascade classifiers for detecting faces in images. Deep Learning Models: Uses deep learning-based models such as MTCNN or SSD for more accurate face detection. Face Recognition Siamese Networks: Employs Siamese networks to learn a similarity metric for face verification. ArcFace: Uses ArcFace for deep face recognition by optimizing the angular margin between face embeddings. Data Preparation Face Images: Collect face images and label them appropriately for training the recognition model. Data Augmentation: Optionally apply data augmentation techniques to increase the diversity of the training data.