Hair detection, segmentation, and hairstyle classification in the wild

Muhammad, U. R., Svanera, M. , Leonardi, R. and Benini, S. (2018) Hair detection, segmentation, and hairstyle classification in the wild. Image and Vision Computing, 71, pp. 25-37. (doi: 10.1016/j.imavis.2018.02.001)

208866.pdf - Accepted Version



Hair highly characterises human appearance. Hair detection in images is useful for many applications, such as face and gender recognition, video surveillance, and hair modelling. We tackle the problem of hair analysis (detection, segmentation, and hairstyle classification) from unconstrained view by relying only on textures, without a-priori information on head shape and location, nor using body-part classifiers. We first build a hair probability map by classifying overlapping patches described by features extracted from a CNN, using Random Forest. Then modelling hair (resp. non-hair) from high (resp. low) probability regions, we segment at pixel level uncertain areas by using LTP features and SVM. For the experiments we extend Figaro, an image database for hair detection to Figaro1k, a new version with more than 1000 manually annotated images. Achieved segmentation accuracy (around 90%) is superior to known state-of-the-art. Images are eventually classified into hairstyle classes: straight, wavy, curly, kinky, braids, dreadlocks, and short.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Svanera, Dr Michele
Authors: Muhammad, U. R., Svanera, M., Leonardi, R., and Benini, S.
College/School:College of Medical Veterinary and Life Sciences > School of Psychology & Neuroscience
Journal Name:Image and Vision Computing
ISSN (Online):1872-8138
Published Online:15 February 2018
Copyright Holders:Copyright © 2018 Elsevier B.V
First Published:First published in Image and Vision Computing
Publisher Policy:Reproduced in accordance with the publisher copyright policy 71:25-37

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