Evaluation of Transfer Learning-Based Convolutional Neural Networks (InceptionV3 and MobileNetV2) for Facial Skin-Type Classification

Abstrak

Manual classification of facial skin types often suffers from subjectivity and inconsistency due to reliance on human expertise. Accurate identification of skin types is crucial for selecting appropriate skincare solutions. This study evaluates the performance of two transfer-learning-based Convolutional Neural Networks (CNNs), InceptionV3 and MobileNetV2, for classifying facial skin types into four categories: normal, oily, dry, and acne-prone. A total of 1,733 facial images were collected from Kaggle and Roboflow and split into training, validation, and testing sets with a 70:20:10 ratio. Preprocessing involved normalization, augmentation, and resizing based on each model’s input size. Both models were fine-tuned and evaluated using accuracy, precision, recall, and F1-score metrics. InceptionV3 achieved the highest accuracy of 90.12% and a macro F1-score of 89.47%, particularly excelling in identifying normal and acne-prone skin. MobileNetV2 reached 81.15% accuracy and performed well on dry skin types. Confusion matrices and evaluation on new, unseen data confirmed the models’ generalization capabilities, though misclassifications still occurred among visually similar classes. These findings suggest that CNNs with transfer learning provide a robust foundation for developing AI-assisted facial skin-type classification systems, offering potential integration into dermatological applications.