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

TL;DR

This paper introduces a deep learning approach that combines weakly supervised pretraining and multi-annotator finetuning to improve automated facial wrinkle segmentation, aiding skin diagnostics and treatments.

Contribution

It presents a novel method integrating multi-annotator data and transfer learning for more accurate facial wrinkle segmentation using CNNs.

Findings

Transfer learning improves segmentation accuracy.

Multi-annotator data integration enhances model robustness.

Automated wrinkle analysis can support skin diagnostics.

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.