Systematic approach to Face Recognition using CNN with deep learning
Recent advancements in face recognition utilizing convolutional neural networks (CNN) have achieved remarkable accuracy rates of up to 98%, significantly outperforming traditional AI methods. CNNs enhance face detection through appearance-based techniques and feature detection of facial components like eyes and mouth with feature-invariant strategies. Key methodologies include data augmentation and transfer learning for effective implementation. The face recognition process involves face detection, alignment, feature extraction, and classification, leveraging CNN and Haar Cascade techniques. With essential layers like convolutional and fully connected layers, CNN efficiently processes images, reduces dimensionality, and addresses challenges like the vanishing gradient problem, ultimately improving recognition accuracy and efficiency.
