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A Deep Learning Approach to Hair Segmentation and Color Extraction from Facial Images

2018; Springer Science+Business Media; Linguagem: Inglês

10.1007/978-3-030-01449-0_37

1611-3349

Diana Borza, Tudor Alexandru Ileni, Adrian Sergiu Dărăbant,

Image Enhancement Techniques

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.