Guideline of Personalized Facial Makeup Using a Hierarchical Cascade Classifier

Abstract

By considering skin color, occasion and dress color as input data, this paper proposes a hierarchical cascade classifier to develop a guideline of personalized facial makeup. Although the makeup recommendation system was previously studied in many researches, but the suggestion cannot be applied for a person accurately in real situation. Color tone based on color wheel theory for facial makeup and color selection from individual skin tone were employed in this study. There were two stages of the hierarchical cascade classifier. The first stage was relied on a rule-based classification procedure, in which rules can be generated by investigating input data within the scope of this research together with the data from a professional makeup artist and 250 face images with makeup originated by makeup experts, resulting in primary color of eye shadow, cheek blush color, and lipstick color. Next, machine learning concept was used as the second stage of the hierarchical cascade classifier to indicate secondary color of eye shadow and alternative lipstick color corresponding to a feature vector. Six classification models, which are Multi-Layer Perceptron, Logistic Regression classifier, Support Vector Machine, Decision Tree, k-nearest neighbor classifier, and Naïve Bayes classifier were selected in this study. From the experimental results, the mixture of rule-based classifier and Multi-Layer Perceptron was suitable to be used as a guideline of personalized facial makeup.