Facial wrinkle detection plays a crucial role in cosmetic dermatology. Precise manual segmentation of facial wrinkles is challenging and time-consuming, with inherent subjectivity leading to inconsistent results among graders. To address this issue, we propose two solutions. First, we build and release the first public facial wrinkle dataset, `FFHQ-Wrinkle', an extension of the NVIDIA FFHQ dataset. This dataset includes 1,000 images with human labels and 50,000 images with automatically generated weak labels. This dataset can foster the research community to develop advanced wrinkle detection algorithms. Second, we introduce a training strategy for U-Net-like encoder-decoder models to detect wrinkles across the face automatically. Our method employs a two-stage training strategy: texture map pretraining and finetuning on human-labeled data. Initially, we pretrain models on a large dataset with weak labels (N=50k) or masked texture maps generated through computer vision techniques, without human intervention. Subsequently, we finetune the models using human-labeled data (N=1k), which consists of manually labeled wrinkle masks. During finetuning, the network inputs a combination of RGB and masked texture maps, comprising four channels. We effectively combine labels from multiple annotators to minimize subjectivity in manual labeling. Our strategies demonstrate improved segmentation performance in facial wrinkle segmentation both quantitatively and visually compared to existing pretraining methods.

本研究解决了面部皱纹检测中手动分割困难和主观性带来的不一致性问题。通过构建第一个公开的面部皱纹数据集`FFHQ-Wrinkle`以及提出的双阶段训练策略，本文显著提升了皱纹分割的准确性和效果，促进了相关算法的发展。此研究的显著发现为美容皮肤科提供了一种有效的自动皱纹检测方法，具有广泛的应用潜力。

面部皱纹分割在美容皮肤科中的应用：基于纹理图的弱监督预训练