Figaro, Hair Detection and Segmentation in the Wild

Svanera, M. , Muhammad, U. R., Leonardi, R. and Benini, S. (2016) Figaro, Hair Detection and Segmentation in the Wild. In: 2016 IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, USA, 25-28 Sep 2016, pp. 933-937. ISBN 9781467399616 (doi: 10.1109/ICIP.2016.7532494)

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Hair is one of the elements that mostly characterize people appearance. Being able to detect hair in images can be useful in many applications, such as face recognition, gender classification, and video surveillance. To this purpose we propose a novel multi-class image database for hair detection in the wild, called Figaro. We tackle the problem of hair detection without relying on a-priori information related to head shape and location. Without using any human-body part classifier, we first classify image patches into hair vs. non-hair by relying on Histogram of Gradients (HOG) and Linear Ternary Pattern (LTP) texture features in a random forest scheme. Then we obtain results at pixel level by refining classified patches by a graph-based multiple segmentation method. Achieved segmentation accuracy (85%) is comparable to state-of-the-art on less challenging databases.

Item Type:Conference Proceedings
Glasgow Author(s) Enlighten ID:Svanera, Dr Michele
Authors: Svanera, M., Muhammad, U. R., Leonardi, R., and Benini, S.
College/School:College of Medical Veterinary and Life Sciences > School of Psychology & Neuroscience
Published Online:08 December 2016
Copyright Holders:Copyright © 2016 IEEE
First Published:First published in 2016 IEEE International Conference on Image Processing (ICIP): 933-937
Publisher Policy:Reproduced in accordance with the publisher copyright policy

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