Accurate analysis and classification of facial attributes are essential in various applications, from human-computer interaction to security systems. In this work, a novel approach to enhance facial classification and recognition tasks through the integration of 3D facial models with deep learning methods was proposed. We extract the most useful information for various tasks using the 3D Facial Model, leading to improved classification accuracy. Combining 3D facial insights with ResNet architecture, our approach achieves notable results: 100% individual classification, 95.4% gender classification, and 83.5% expression classification accuracy. This method holds promise for advancing facial analysis and recognition research.
@article{arxiv.2312.05219,
title = {Enhancing Facial Classification and Recognition using 3D Facial Models and Deep Learning},
author = {Houting Li and Mengxuan Dong and Lok Ming Lui},
journal= {arXiv preprint arXiv:2312.05219},
year = {2025}
}
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arXiv admin note: text overlap with arXiv:1903.08527 by other authors