Improving Facial Landmark Detection Accuracy and Efficiency with
  Knowledge Distillation
Zong-Wei Hong Yu-Chen Lin
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.