2024/08/19 by Junho Moon, Moon, Junho, Haejun Chung +3 · 1 voice
Business, Management and Accounting · Computer Science · Psychology · #Artificial Intelligence (cs.AI) #Body Image and Dysmorphia Studies #Computer Vision and Pattern Recognition (cs.CV) #Consumer Perception and Purchasing Behavior #FOS: Computer and information sciences #Face recognition and analysis #Machine Learning (cs.LG) #cs.AI #cs.CV #cs.LG
paper · pdf · doi:10.48550/arxiv.2408.10060
openalex publication_date 2024/08/19 · arxiv published 2024/08/19 · arxiv updated 2024/11/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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. It includes 1,000 images with human labels and 50,000 images with automatically generated weak labels. This dataset could serve as a foundation for the research community to develop advanced wrinkle detection algorithms. Second, we introduce a simple training strategy utilizing texture maps, applicable to various segmentation models, to detect wrinkles across the face. Our two-stage training strategy first pretrain models on a large dataset with weak labels (N=50k), or masked texture maps generated through computer vision techniques, without human intervention. We then finetune the models using human-labeled data (N=1k), which consists of manually labeled wrinkle masks. The network takes as input a combination of RGB and masked texture map of the image, comprising four channels, in finetuning. 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. The dataset is available at https://github.com/labhai/ffhq-wrinkle-dataset.