The beauty and skincare industry is one of the sectors that artificial intelligence is changing. One promising application includes using AI-led skin analysis to recommend products in a personalized way. This article will address how the model could work, its potential to enhance the user experience, and the ethical considerations to be made for responsible implementation from a generic and simplified view.
AI Model Design for Skin Analysis
An AI model that can analyse customers’ skin and provide personalised branded product recommendations. A powerful approach involves training autoencoder models to extract image features. The model mentioned in the paper Using Images to Find Context-Independent Word Representations in Vector Space is not specifically designed for skin care, it may be inspiring for how the model could be built. “We train an autoencoder model to find meaningful representations for the images in the dataset. The model consists of a five-layer encoder-decoder architecture with a latent state size of 32.”(Harsh Kumar (11-2024))
Key model features could include:
– Skin Feature Extraction: The AI could detect skin texture, hydration, and acne marks using deep learning models. These insights would guide product recommendation in order of priority and ensure a personalised user experience.
– Priority-Based Suggestions: The system could tailor recommendations for optimal skincare routines by ranking detected skin issues.
Improving Customer Experience through AI
Have you ever seen the machines they use in Korea for skin detection and recommending the best treatments? Korean treatments are known all over the world for using science-based suggestions. Recent research highlights several technical approaches:
– Skin Analysis Using CNNs: Research from An AI-Assisted Skincare Routine Recommendation System in XR shows that convolutional neural networks (CNNs) can analyze skin features, generating tailored product suggestions (Shariff et al., 2023).
– Facial Landmark Detection: The system could employ the ‘Haar Cascade’ classifier for face detection, to identify facial regions prone. Precise detection would allow more accurate product recommendations (Shariff et al., 2023).
– Training Methodology: Using transfer learning with the VGG16 model—known for its compact structure and modular design—could accelerate development while improving accuracy in skin condition detection (Shariff et al., 2023).
Ethical Considerations for AI in Skincare
While AI-driven skincare recommendations offer exciting possibilities, they raise critical ethical questions in skincare, mainly questions related to fairness and bias, data privacy and AI accountability. AI models may reflect unintentional bias based on training, affecting possible product recommendations, although Foreo’s product line of high-end skincare accessories may avoid fairness issues, this area warrants deeper review.
One of the most significant challenges is data privacy, as collecting and storing personal information, such as skin images, requires a robust AI governance framework to protect the information. Responsibility is shared, not only for developers, it also includes marketers, researchers, and regulatory bodies. As it is mentioned in the Towards a Practical Ethics of Generative AI “Integrating ethical considerations into AI governance is essential for ensuring fairness, transparency, and accountability”(Gao et al., 2023).
Conclusion
AI-led skincare analytics have the potential to transform the beauty industry through personalised product recommendations. While technical advances such as CNNs and transfer learning enable accurate detection and personalised recommendations, companies need to invest in AI governance. Balancing innovation with ethical responsibility in the beauty industry can make a significant difference, by nurturing user trust and creating personalised user experiences.
References
Geert Hofman (11-2024) Towards a Practical Ethics of Generative AI in Creative Production Processes arXiv:2412.03579 [cs.CY]
Harsh Kumar (11-2024) Using Images to Find Context-Independent Word Representations in Vector Space. arXiv:2412.03592 [cs.CL]
Gowravi Malalur Rajegowda1, Yannis Spyridis2, Barbara Villarini3 and Vasileios Argyriou1.(2023). An AI-Assisted Skincare Routine Recommendation System in XR.
