Continuous Face Authentication in Real Time using Web-Cam

Abstract

Abstract: Face authentication is becoming increasingly important for security and identification purposes, but existing systems are often limited by factors such as lighting, facial expressions, and changes in appearance over time. This paper proposes a solution that combines deep learning techniques with adaptive face representation, enabling the system to continuously authenticate the user even in challenging conditions. this research paper also presents a newly developed model for face recognition. The model is based on a deep learning approach that combines convolutional neural networks (CNN) with a recurrent neural network (RNN) architecture. This approach allows the model to not only extract high-level features from facial images, but also to capture temporal dependencies between frames, resulting in a more accurate and robust recognition system. The model was trained and tested on several benchmark datasets, and achieved state-of-the-art performance in terms of accuracy and efficiency. Overall, The proposed model combines convolutional neural networks with a recurrent neural network architecture, allowing it to extract high-level features and capture temporal dependencies between frames for improved accuracy and robustness. The model was trained and tested on a benchmark dataset, achieving an impressive test accuracy of 100%, as well as a perfect precision, recall, and F1-score. Additionally, the paper compares the results of the proposed model to those of a traditional SVM model, which also achieved a perfect accuracy, precision, recall, and F1-score. The comparison highlights the effectiveness of the proposed approach and demonstrates its potential as a highly reliable and efficient authentication solution.