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Fast In-the-Wild Hair Segmentation and Color Classification

Topics: Color and Texture Analyses; Deep Learning for Visual Understanding ; Entertainment Imaging Applications; Face and Expression Recognition; Human and Computer Interaction; Image Formation, Acquisition Devices and Sensors; Machine Learning Technologies for Vision; Segmentation and Grouping

Authors: Tudor Ileni 1 ; Diana Borza 2 and Adrian Darabant 1

Affiliations: 1 Department of Computer Science, Faculty of Mathematics and Computer Science, Babes-Bolyai University, Cluj-Napoca and Romania ; 2 Computer Science Department, Technical University of Cluj-Napoca and Romania

ISBN: 978-989-758-354-4

Keyword(s): Hair Segmentation, Hair Color, Fully Convolutional Neural Network, Histograms, Neural Network.

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%.

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Paper citation in several formats:
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 - Volume 4: VISAPP, ISBN 978-989-758-354-4, pages 59-66. DOI: 10.5220/0007250500590066

@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 - Volume 4: VISAPP,},
year={2019},
pages={59-66},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007250500590066},
isbn={978-989-758-354-4},
}

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - 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

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