Fast In-the-Wild Hair Segmentation and Color Classification

摘要

In this paper we address the problem of hair segmentation and hair color classification in facial images using a machine learning approach based on both convolutional neural networks and classical neural networks. Hair with its color shades, shape and length represents an important feature of the human face and is used in domains like biometrics, visagisme (the art of aesthetically matching fashion and medical accessories to the face region), hair styling, fashion, etc. We propose a deep learning method for accurate and fast hair segmentation followed by a histogram feature based classification of the obtained hair region on five color classes. We developed a hair and face annotation tool to enrich the training data. The proposed solutions are trained on publicly available and own annotated databases. The proposed method attained a hair segmentation accuracy of 91.61% and a hair color classification accuracy of 89.6%.