Automatic Skin Tone Extraction for Visagism Applications

In this paper we propose a skin tone classification system on three skin colors: dark, medium and light. We work on two methods which don’t require any camera or color calibration. The first computes color histograms in various color spaces on representative facial sliding patches that are further combined in a large feature vector. The dimensionality of this vector is reduced using Principal Component Analysis a Support Vector Machine determines the skin color of each region. The skin tone is extrapolated using a voting schema. The second method uses Convolutional Neural Networks to automatically extract chromatic features from augmented sets of facial images. Both algorithms were trained and tested on publicly available datasets. The SVM method achieves an accuracy of 86.67%, while the CNN approach obtains an accuracy of 91.29%. The proposed system is developed as an automatic analysis module in an optical visagism system where the skin tone is used in an eyewear virtual try-on software that allows users to virtually try glasses on their face using a mobile device with a camera. The system proposes only esthetically and functionally fit frames to the user, based on some facial features –skin tone included.