Facial makeup detection via selected gradient orientation of entropy information
This work presents a novel facial makeup detection method, which includes four steps: entropy information computation, feature extraction, feature selection and classification. To carry out this objective, first all face images are subject to the entropy information computation. Once the entropy images of faces are obtained, a feature extraction step is applied to the entropy images instead of original face images. The extracted features are further processed to reduce the redundant information on the feature vector, which is done by a feature selection procedure. A statistical analysis approach is chosen to realize this feature selection purpose, which aims to lower the feature dimension and maintain higher discrimination. In the last step, the makeup is detected by classifying faces into two groups: makeup and no-makeup. The experimental results on two databases indeed demonstrate the superiority of the proposed method.