Real Time Face Detection and Recognition through Advanced Convolutional Neural Network Architectures
Author : Dr. K Kalyani and V Nivetha
Abstract :
The proposed system presents a real-time face recognition framework built using Convolutional Neural Networks (CNNs) and advanced deep learning techniques to accurately identify and authenticate individuals. It incorporates a robust preprocessing pipeline including face detection, alignment, normalization, and filtering to ensure consistent performance across diverse environments. Facial images are converted into high-dimensional feature vectors, and similarity is measured using distance metrics with an optimized threshold for verification. The model is trained using multiple datasets with proper preprocessing, positive and negative sample generation, and systematic hyperparameter tuning to improve accuracy and convergence. Designed to handle variations such as facial expressions and contextual attributes, the system offers a scalable and efficient solution for practical face recognition applications, while also considering future improvements in interpretability, dataset balance, and ethical concerns.
Keywords :
Face Recognition, Convolutional Neural Networks (CNN), Deep Learning, Feature Extraction, Image Preprocessing. Distance Metrics, Hyperparameter Tuning, Real-Time Recognition.
