Fast In-the-Wild Hair Segmentation and Color Classification

Tudor Alexandru Ileni, Diana Laura Borza, Adrian Sergiu Darabant

2019

Abstract

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%.

Paper Citation

in Harvard Style

Ileni T., Borza D. and Darabant A. (2019). Fast In-the-Wild Hair Segmentation and Color Classification. In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 4: VISAPP; ISBN 978-989-758-354-4, SciTePress, pages 59-66. DOI: 10.5220/0007250500590066

in Bibtex Style

@conference{visapp19,
author={Tudor Alexandru Ileni and Diana Laura Borza and Adrian Sergiu Darabant},
title={Fast In-the-Wild Hair Segmentation and Color Classification},
booktitle={Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 4: VISAPP},
year={2019},
pages={59-66},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007250500590066},
isbn={978-989-758-354-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 4: VISAPP
TI - Fast In-the-Wild Hair Segmentation and Color Classification
SN - 978-989-758-354-4
AU - Ileni T.
AU - Borza D.
AU - Darabant A.
PY - 2019
SP - 59
EP - 66
DO - 10.5220/0007250500590066
PB - SciTePress