Systematic Review: Recent Advancements in Deep Learning Techniques for Facial Feature Recognition
DOI:
https://doi.org/10.56294/dm2025658Keywords:
Convolutional Neural Networks, Long Short-Term Memory Networks, Generative Adversarial Networks, Hybrid Deep Learning Approaches, Human-Computer Interaction, Multi-modal SystemsAbstract
Deep Learning is a rapidly evolving field with critical contributions to various domains including security, healthcare, and human — computer interaction, etc. It reviews the significant developments in the area of facial recognition using deep learning techniques. It explains deep learning models such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory Networks (LSTMs), and Generative Adversarial Networks (GANs), as well as hybrid models and transfer learning uses. It also addresses technical, ethical, and legal challenges that arise for facial analysis systems and emphasizes the need for real-time processing, multi-modal systems, and robust algorithms to improve the technical accuracy and fairness of facial analysis.
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