利用知识蒸馏提升面部关键点检测的准确性与效率
Improving Facial Landmark Detection Accuracy and Efficiency with Knowledge Distillation
AI总结：
本文提出面向面部关键点检测的知识蒸馏方法，将大模型知识迁移至轻量模型，以在多样表情、朝向和光照下兼顾高精度与嵌入式实时性能，并在 IEEE ICME 2024 PAIR 竞赛 165 支参赛队伍中获得第 6 名。
摘要：
计算机视觉领域在面部关键点检测方面取得了显著进展，该技术在增强现实、人脸识别和情感分析等各类应用中变得日益重要。与专注于识别物体和勾勒边界的目标检测或语义分割不同，面部关键点检测旨在精确定位和跟踪关键面部特征。然而，由于面部特征的复杂性，尤其是在动态环境中，在计算资源有限的嵌入式系统上部署基于深度学习的面部关键点检测模型面临挑战。此外，确保在不同种族和表情下的鲁棒性也带来了进一步障碍。现有数据集往往缺乏对面部细微差别的全面表征，尤其是在台湾人群等群体中。本文提出了一种新方法，通过开发一种知识蒸馏方法来应对这些挑战。通过将知识从较大模型迁移到较小模型，我们旨在创建专为面部关键点检测任务定制的轻量而强大的深度学习模型。我们的目标是设计能够在不同条件下（包括多样的表情、朝向和光照环境）准确定位面部关键点的模型。最终目标是实现适合在嵌入式系统上部署的高精度和实时性能。该方法已成功实施，并在 IEEE ICME 2024 PAIR 竞赛的 165 名参赛者中取得了第 6 名的成绩。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
