A Deep Learning Approach to Hair Segmentation and Color Extraction from Facial Images
| Autor: | Diana Borza, Tudor Alexandru Ileni, Adrian Sergiu Darabant |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
021110 strategic
defence & security studies Artificial neural network Pixel Computer science business.industry Deep learning Hair analysis ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 0211 other engineering and technologies Multi-task learning Pattern recognition 02 engineering and technology GeneralLiterature_MISCELLANEOUS Random forest Face (geometry) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Segmentation Artificial intelligence business ComputingMethodologies_COMPUTERGRAPHICS |
| Zdroj: | Advanced Concepts for Intelligent Vision Systems ISBN: 9783030014483 ACIVS |
| DOI: | 10.1007/978-3-030-01449-0_37 |
| Popis: | In this paper we tackle the problem of hair analysis in unconstrained images. We propose a fully convolutional, multi-task neural network to segment the image pixels into hair, face and background classes. The network also decides if the person is bald or not. The detected hair pixels are analyzed by a color recognition module which uses color features extracted at super-pixel level and a Random Forest Classifier to determine the hair tone (black, blond, brown, red or white grey). To train and test the proposed solution, we manually segment more than 3500 images from a publicly available dataset. The proposed framework was evaluated on three public databases. The experiments we performed together with the hair color recognition rate of 92% demonstrate the efficiency of the proposed solution. |
| Databáze: | OpenAIRE |
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