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arXiv · 2408.09952

Weakly Supervised Pretraining and Multi-Annotator Supervised Finetuning for Facial Wrinkle Detection

Ik Jun Moon·Junho Moon·Ikbeom Jang
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Abstract

1. Research question: With the growing interest in skin diseases and skin aesthetics, the ability to predict facial wrinkles is becoming increasingly important. This study aims to evaluate whether a computational model, convolutional neural networks (CNN), can be trained for automated facial wrinkle segmentation. 2. Findings: Our study presents an effective technique for integrating data from multiple annotators and illustrates that transfer learning can enhance performance, resulting in dependable segmentation of facial wrinkles. 3. Meaning: This approach automates intricate and time-consuming tasks of wrinkle analysis with a deep learning framework. It could be used to facilitate skin treatments and diagnostics.

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Ik Jun Moon, Junho Moon, Ikbeom Jang. 2024-08-19. Weakly Supervised Pretraining and Multi-Annotator Supervised Finetuning for Facial Wrinkle Detection. https://arxiv.org/abs/2408.09952

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