Speaker
Description
Facial detection is a rapidly evolving field, driven by recent advancements in artificial intelligence (AI) and machine learning. This presentation explores the current techniques employed in facial detection, with a focus on computer vision algorithms, such as convolutional neural networks (CNNs), which have revolutionized this discipline.
We will begin with an introduction to the fundamental concepts of facial detection, explaining the various stages of the process, from face localization to feature extraction. We will then discuss traditional methods, such as Haar cascade classifiers, before delving into AI-based approaches. Modern models, including CNN architectures and transfer learning techniques, significantly enhance the accuracy and robustness of facial detection, even under challenging lighting conditions or with partially obscured faces. We will also address the datasets used to train these models and the associated challenges, such as data diversity and algorithmic biases. Finally, we will examine practical applications of facial detection across various domains, including security, marketing, and healthcare. We will conclude by discussing the ethical implications and regulatory considerations surrounding the use of these technologies, emphasizing the importance of responsible and transparent development.
This presentation aims to provide a comprehensive overview of AI techniques applied to facial detection while encouraging reflection on their societal implications.
| Abstract Category | Computing & 4IR |
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