Facial Skin Classification Using Convolutional Neural Networks

Facial skin assessment is crucial for a number of fields including the make-up industry, dermatology and plastic surgery. This paper addresses skin classification techniques which use conventional machine learning and state-of-the-art Convolutional Neural Networks to classify three types of facial skin patches, namely normal, spots and wrinkles. This study aims to accomplish the pivotal work on the basis of these three classes to provide the collective facial skin quality score. In this work, we collected high quality face images of people from different ethnicities to create a derma dataset. Then, we outlined the skin patches of 100 \(\times \) 100 resolution in the three pre-decided classes. With extensive parameter tuning, we ran a number of computer vision experiments using both traditional machine learning and deep learning techniques for this 3-class classification. Despite the limited dataset, GoogLeNet outperforms the Support Vector Machine approach with Accuracy of 0.899, F-Measure of 0.852 and Matthews Correlation Coefficient of 0.779. The result shows the potential use of deep learning for non-clinical skin images classification, which will be more promising with a larger dataset.