Skin analysis and recommendation system

Ng, Yong Shen (2025) Skin analysis and recommendation system. Final Year Project, UTAR.

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Abstract

Perceived opacity and uneven performance of AI skin analysis tools drive users toward unguided, trial-and-error use of skincare products, amplifying ingredient conflicts and delaying improvement. To address this issue, this project develops a skin analysis and recommendation system that delivers a trusted skin analysis model alongside a content-based recommendation method. Firstly, the system provides real-time face detection using the Android library and produces results from the deployed model accompanied by accuracy metrics. This enables use across diverse environments and ensures reliable results through guidance on capture quality and low-confidence flags. To achieve robust skin analysis process, Several AI models, including Classic CNN, EfficientNetB0, ResNet50, and GPT assistant–based models, were trained and tested on datasets for acne severity level and skin type classification. Among these, the YOLOv8 model demonstrated superior performance, achieving testing accuracies of 76.2% for acne severity and 64.0% for skin type, outperforming all other models. The integration of GPT-4o introduces a GPT assistant–based approach that complements traditional deep learning by enhancing interpretability and flexibility in prediction tasks through concise rationales and uncertainty cues. Secondly, the system generates skincare products recommendations based on either the current analysis results or user-specified conditions through content-based filtering. The content-based filtering process applied predefined features rules, whereby acne severity level and skin type govern the selection of recommended product attributes. Users can then view detailed information about these products, with the system generating ingredient-based justifications to support informed decision-making. To enhance personalization, the system has been integrating LLM models, allowing users to select either the Gemini API or the OpenAI API, which provide additional insights and recommendations. Thirdly, selected products can finally be added to a personalized skincare routine, where the system performs AI analysis to evaluate overall compatibility of the routine with the user’s skin condition. Fourthly, the system further aggregates skin analysis results to track progress trends for acne severity and skin type in overall, daily and monthly basis, where the system will perform descriptive analysis on progress trend to provide user with useful insight on their skin condition. This project successfully developed multiple module which are skin analysis, product recommendation, skincare routine management, and skin progress tracking to provide a seamless and reliable skin analysis and skincare recommendation experience while ensuring a user-centric and privacy-conscious mobile design.

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