Real-time semantic segmentation plays a crucial role in industrial applications, such as autonomous driving, the beauty industry, and so on. It is a challenging problem to balance the relationship between speed and segmentation performance. To address such a complex task, this paper introduces an efficient convolutional neural network (CNN) architecture named HLNet for devices with limited resources. Based on high-quality design modules, HLNet better integrates high-dimensional and low-dimensional information while obtaining sufficient receptive fields, which achieves remarkable results on three benchmark datasets. To our knowledge, the accuracy of skin tone classification is usually unsatisfactory due to the influence of external environmental factors such as illumination and background impurities. Therefore, we use HLNet to obtain accurate face regions, and further use color moment algorithm to extract its color features. Specifically, for a 224 × 224 input, using our HLNet, we achieve 78.39% mean IoU on Figaro1k dataset at over 17 FPS in the case of the CPU environment. We further use the masked color moment for skin tone grade evaluation and approximate 80% classification accuracy demonstrate the feasibility of the proposed method.
https://github.com/JACKYLUO1991/FaceParsing.
It is worth noting that some training sets are mistaken for test sets in image file copying, which leads to high results in arXiv. The current version has been corrected.
@article{feng2020hlnet,
title={HLNet: A Unified Framework for Real-Time Segmentation and Facial Skin Tones Evaluation},
author={Feng, Xinglong and Gao, Xianwen and Luo, Ling},
journal={Symmetry},
volume={12},
number={11},
pages={1812},
year={2020},
publisher={Multidisciplinary Digital Publishing Institute}
}
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