Data-Driven Beauty: Unleashing the Power of Deep Learning in Skincare

The rapid growth of the skincare industry and an abundance of available products make consumers frequently struggle to select appropriate cosmetics for their specific skin types and needs. Accurately measuring skin type is essential but difficult. This study describes a new method that employs advanced techniques such as the Gray-Level Co-occurrence Matrix (GLCM) for texture information extraction and Graph Neural Networks (GNNs) to provide tailored skincare suggestions. After preprocessing facial photos, GLCM extracts spatial texture data to feed into the GNN model. The GNN treats various facial regions as nodes in a graph, recording detailed interactions between them to allow for accurate skin type classification. GNNs outperform classic convolutional and recurrent neural networks in this task because they account for spatial dependencies. Evaluations show that the system outperforms previous neural network architectures such as CNN and RNN, with 90% accuracy, 88% recall, and 89% precision. The proposed GLCM and GNN strategy simplifies product selection, increasing consumer satisfaction and encouraging optimal skin health through personalized routines. This data-driven approach reimagines beauty shopping and how consumers navigate the crowded skincare market by automating personal skin analysis and making personalized recommendations based on user characteristics.