Laura L. Zimny ¹ and Andy Liang ² , ¹ USA, ² California State University Los Angeles, USA

This paper addresses the technical challenge of bridging the gap between unstructured product data and personalized skincare guidance. The proposed solution, GlowLab, is an integrated mobile platform that synthesizes diverse data streams to optimize skincare routines efficiently and safely. The system is engineered around three core components: a persistent Data Service for tracking longitudinal skin health, a computer-vision enabled Product Analysis System that parses ingredient lists via the OpenAI API, and a Recommendation Engine that dynamically cross-references these inputs against user sensitivities[1]. Unlike systems relying on invasive biometric surveillance, this project addresses privacy and design challenges by utilizing secure local storage and user-reported data. Technical testing of the image recognition module demonstrated a 100% success rate across 20 test cases, confirming the system's ability to correctly parse complex labels into structured data [2]. The result is a robust, responsive application that transforms static ingredient data into dynamic, actionable health insights, providing immediate value for everyday consumers seeking safe, personalized guidance.

Skincare AI, Ingredient Analysis, Personalization, Mobile App.