Facial Skin Type Analysis Using Few-shot Learning with Prototypical Networks

Abstract

Facial skin type analysis is a critical task in several fields, including dermatology, cosmetics, and biometrics, and has been the subject of significant research in recent years. Traditional facial skin type analysis approaches rely on large, labeled datasets, which can be time-consuming and costly to collect. This study proposes a novel few-shot learning (FSL) approach for facial skin type analysis that can accurately classify skin types with limited labeled data. A diverse dataset of facial images with varying skin tones and conditions was curated. The proposed approach leverages pre-trained deep neural networks and an FSL algorithm based on prototypical networks (PNs) and matching networks (MNs) to address the challenge of limited labeled data. Importantly, this study has significant implications for improving access to dermatological care, especially in underserved populations, as many individuals are unaware of their skin type, which can lead to ineffective or even harmful skincare practices. Our approach can help individuals quickly determine their skin type and develop a personalized skincare routine based on their unique skin characteristics. The results of our experiments demonstrate the effectiveness of the proposed approach. PNs achieved the highest accuracy in the 2-way, 10-shot, 15-query scenario with an accuracy of 95.78 ± 2.79%, while MNs achieved the highest accuracy of 90.33 ± 4.10% in the 2-way, 5-shot, 10-query scenario. In conclusion, this study highlights the potential of FSL and deep neural networks to overcome the limitations of traditional approaches to facial skin analysis, offering a promising avenue for future research in this field.