Advanced AI Approaches for Reliable 3D Face Recognition in Dynamic Pose and Expression Scenarios
DOI:
https://doi.org/10.62431/rqyjr496Keywords:
3D Face Recognition, Artificial Intelligence (AI), Attention Mechanisms, Biometric Authentication, Convolutional Neural Networks (CNNs), Deep Learning, Facial Expression, Generative Adversarial Networks (GANs), Pose Variation, Robust RecognitionAbstract
Recent progress in artificial intelligence (AI), especially in deep learning (DL), has led to significant improvements in 3D face recognition technologies. However, achieving consistent performance across a wide range of facial poses and expressions remains a challenging task due to high intra-class variation and the complex, non-linear nature of 3D facial structures. This paper explores how state-of-the-art AI methods—particularly convolutional neural networks (CNNs), generative adversarial networks (GANs), and attention-based mechanisms (ABMs)—have contributed to enhancing the robustness and precision of 3D face recognition systems. These advanced models effectively learn pose-invariant characteristics and generate standardized facial representations, outperforming traditional techniques in uncontrolled conditions. The study provides an overview of current advancements, evaluates performance using prominent benchmark datasets, and discusses key limitations and prospective research directions for deploying AI-powered 3D face recognition in real-world scenarios.
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