Enhanced Face Reconstruction and Recognition System with Audio- Visual Fusion

Prathika Muthu, Damodharan Asaithambi Ramani, Jenifer Arputham

2025

Abstract

Deep Learning-Based Audio-Visual Fusion Approach for Enhanced Face Reconstruction and Recognition System: A New Paradigm for Improving Accuracy of Face Reconstruction and Recognition. The challenging factors in this area, namely illumination, pose, and expression, have been addressed by Local Binary Pattern over Radon Transform audio feature extraction that are fused with visual data. The features are encoded with an autoencoder while the CNN-based decoder reconstructs facial images of high quality from noisy or incomplete data. This innovative system will improve the accuracy of recognition in any scenario, making it valuable for forensic analysis, security, and adaptive user interfaces. Audio-visual fusion can be used to perform holistic facial analysis, which is far beyond the traditional visual-only approach. Advanced neural networks provide much better performance than existing approaches. Future extensions could include thermal imaging, depth data, or real-time processing for dynamic environments. This system, based on deep learning techniques, marks an important step in facial recognition technology with great potential applications across various domains that require reliable and precise facial identification.

Paper Citation

in Harvard Style

Muthu P., Asaithambi Ramani D. and Arputham J. (2025). Enhanced Face Reconstruction and Recognition System with Audio- Visual Fusion. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 887-894. DOI: 10.5220/0013734400004664

in Bibtex Style

@conference{incoft25,
author={Prathika Muthu and Damodharan Asaithambi Ramani and Jenifer Arputham},
title={Enhanced Face Reconstruction and Recognition System with Audio- Visual Fusion},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT},
year={2025},
pages={887-894},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013734400004664},
isbn={978-989-758-763-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT
TI - Enhanced Face Reconstruction and Recognition System with Audio- Visual Fusion
SN - 978-989-758-763-4
AU - Muthu P.
AU - Asaithambi Ramani D.
AU - Arputham J.
PY - 2025
SP - 887
EP - 894
DO - 10.5220/0013734400004664
PB - SciTePress