The domain of computer vision has experienced significant advancements in
facial-landmark detection, becoming increasingly essential across various
applications such as augmented reality, facial recognition, and emotion
analysis. Unlike object detection or semantic segmentation, which focus on
identifying objects and outlining boundaries, faciallandmark detection aims to
precisely locate and track critical facial features. However, deploying deep
learning-based facial-landmark detection models on embedded systems with
limited computational resources poses challenges due to the complexity of
facial features, especially in dynamic settings. Additionally, ensuring
robustness across diverse ethnicities and expressions presents further
obstacles. Existing datasets often lack comprehensive representation of facial
nuances, particularly within populations like those in Taiwan. This paper
introduces a novel approach to address these challenges through the development
of a knowledge distillation method. By transferring knowledge from larger
models to smaller ones, we aim to create lightweight yet powerful deep learning
models tailored specifically for facial-landmark detection tasks. Our goal is
to design models capable of accurately locating facial landmarks under varying
conditions, including diverse expressions, orientations, and lighting
environments. The ultimate objective is to achieve high accuracy and real-time
performance suitable for deployment on embedded systems. This method was
successfully implemented and achieved a top 6th place finish out of 165
participants in the IEEE ICME 2024 PAIR competition.

通过知识蒸馏方法，本研究提出了一种创新的方式，可以开发适用于嵌入式系统的轻量级但功能强大的深度学习模型，以应对面部地标检测任务中的挑战，包括动态环境、不同的表情、方向和光照条件。