FIGARO, HAIR DETECTION AND SEGMENTATION IN THE WILD


FIGARO, HAIR DETECTION AND SEGMENTATION IN THE WILD PDF
M. Svanera*, U.R. Muhammad*, R. Leonardi, and S. Benini
IEEE International Conference on Image Processing (ICIP), 2016.

* These authors contributed equally to this work.

Abstract

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.

Figaro Dataset

database
Figaro is a multi-class image database for hair detection in the wild. It contains 840 unconstrained view images with persons, subdivided into seven different hairstyles classes (straight, wavy, curly, kinky, braids, dreadlocks, short-men), where each image is provided with the related manually segmented hair mask.

BibTeX

@inproceedings{SvaneraUmar2016Figaro,
          title={FIGARO, HAIR DETECTION AND SEGMENTATION IN THE WILD},
          author={Svanera, Michele and Muhammad, Umar Riaz and Benini, Sergio and Leonardi, Riccardo},
          booktitle={2016 IEEE International Conference on Image Processing (ICIP)},
          year={2016},
          organization={IEEE}
          }