Rule-Based Facial Makeup Recommendation System

Facial makeup style plays a key role in the facial appearance making it more beautiful and attractive. Choosing the best makeup style for a certain face to fit a certain occasion is a full art. Also, foretelling how the face will look like after applying the proposed makeup style requires a high imagination. To solve this problem computationally, an automatic and smart facial makeup recommendation and synthesis system is proposed in this paper. This system starts by classifying the makeup related facial traits that makeup artists consider to decide the makeup style; Then, a rule-based makeup recommendation system is built by creating a knowledge base that models the relation between the facial style attributes and makeup style attributes, taking into account the occasion such as daily makeup or heavy makeup and the desired trend with semantic text explaining logic behind the recommended style. Finally, an automatic facial makeup synthesis system is developed to apply the recommended style on the facial image as well. To this end, a new dataset with 961 different females photos collected and labeled. To evaluate the performance of the proposed system, an extensive experimental analysis is conducted on the automatic facial attributes classification, the recommendation efficiency and the synthesis accuracy under different conditions. The obtained results show the effectiveness and flexibility of the proposed fully automatic framework.