  Modern approaches for semantic segmention usually pay too much attention to
the accuracy of the model, and therefore it is strongly recommended to
introduce cumbersome backbones, which brings heavy computation burden and
memory footprint. To alleviate this problem, we propose an efficient
segmentation method based on deep convolutional neural networks (DCNNs) for the
task of hair and facial skin segmentation, which achieving remarkable trade-off
between speed and performance on three benchmark datasets. As far as we know,
the accuracy of skin tones classification is usually unsatisfactory due to the
influence of external environmental factors such as illumination and background
noise. Therefore, we use the segmentated face to obtain a specific face area,
and further exploit the color moment algorithm to extract its color features.
Specifically, for a 224 x 224 standard input, using our high-resolution spatial
detail information and low-resolution contextual information fusion network
(HLNet), we achieve 90.73% Pixel Accuracy on Figaro1k dataset at over 16 FPS in
the case of CPU environment. Additional experiments on CamVid dataset further
confirm the universality of the proposed model. We further use masked color
moment for skin tones grade evaluation and approximate 80% classification
accuracy demonstrate the feasibility of the proposed scheme.Code is available
at
https://github.com/JACKYLUO1991/Face-skin-hair-segmentaiton-and-skin-color-evaluation.
