Abstract
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.
Abstract (translated)
计算机视觉领域在面部特征检测方面取得了显著的进步，越来越成为各种应用（如增强现实、面部识别和情感分析）中不可或缺的技术。与物体检测或语义分割不同，面部特征检测的目标是精确地定位和跟踪关键面部特征。然而，将基于深度学习的面部特征检测模型部署在具有有限计算资源的嵌入式系统上，会面临由于面部特征的复杂性而导致的挑战。此外，确保在不同种族和表情上具有稳健性也带来了进一步的障碍。现有的数据集通常缺乏对面部细节的全面代表，特别是在像台湾这样的种族中。本文通过开发一种知识蒸馏方法来解决这些挑战。通过将知识从大模型传递到小模型，我们旨在创建专为面部特征检测任务设计的轻量级但强大的深度学习模型。我们的目标是设计出在各种条件下准确定位面部特征的模型，包括不同的表情、方向和光线环境。最终目标是实现适合嵌入式系统的较高准确性和实时性能。该方法在IEEE ICME 2024 PAIR比赛中取得了第六名的成绩。
URL
https://arxiv.org/abs/2404.06029
