The content introduces LAFS, a novel method focusing on self-supervised learning using facial landmarks to improve face recognition accuracy. It explores the effectiveness of landmark-based augmentations and provides insights into few-shot scenarios and large-scale datasets.
In recent years, advancements in face recognition have been driven by techniques like advanced loss functions and specialized network structures. However, the impact of initial parameters, specifically facial representation, has been overlooked in many works.
The proposed LAFS method utilizes self-supervised pretraining with facial landmarks to enhance face recognition models. By incorporating landmark-specific augmentations and addressing challenges like few-shot scenarios, LAFS demonstrates significant improvements over existing methods.
Through experiments and ablation studies, the effectiveness of LAFS is demonstrated on various benchmarks. The results show that self-supervised learning with landmark-based approaches can lead to state-of-the-art performance in face recognition tasks.
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by Zhonglin Sun... pada arxiv.org 03-14-2024
https://arxiv.org/pdf/2403.08161.pdfPertanyaan yang Lebih Dalam