ENHANCED FACE RECOGNITION SYSTEM INTEGRATING GANAUGMENTED CNN FEATURES WITH ICA AND VISION TRANSFORMER

Facial recognition is a high-technology biometric method that is extensively applied as
identity verification, surveillance, and a security system. This paper proposes a hybrid deep
learning-based model that can contribute to improved face and iris recognition accuracy and
efficiency. This is done by first augmenting the datasets through Generative Adversarial
Networks (GANs) that train more synthetic face and iris images to incorporate more and
better diversity to the dataset and to reduce overfitting, after which the dataset is fed into a
Convolutional Neural Network (CNN) to automatically learn and extract deep spatial
features of the augmented images. These extracted features are then narrowed down using
Independent Component Analysis (ICA) to select the most important features, removing
redundant and irrelevant information. The optimized features are then forwarded to a Vision
Transformer (ViT) to be classified by the transformer architecture that takes good
consideration of spatial relationships to accurately determine individual face and iris.
Performance evaluation metrics of the proposed system include accuracy of 0.93%,
precision of 0.938%, recall of 0.930%, and F1-score of 0.9319%, which show that the
proposed system has better recognition performance and strength than the conventional face
recognition methods.