像素级的皮肤分割与面色分级

皮肤是人体最大的器官,面色相对于人体其他生物属性具有更便捷、更稳定的特性。因此,设计一个完整有效的面色分级系统是非常有意义的。本文中,面色分级系统被分为皮肤分割和面色分级2部分。针对皮肤分割任务,在生成对抗网络框架下搭建了一个多尺度特征融合网络,相对于传统的语义分割网络,本文的分割模型充分地利用了每一层特征图的信息。在面色分级实验中,首先在归一化rgb、HSV和Lab颜色空间下使用1 000幅图像分别训练了支持向量机(SVM)和BP神经网络分类器,128幅皮肤图像被用作测试集,正确率在73%～76%;之后将颜色特征与皮肤区域纹理特征融合进行学习,使用SVM分类的正确率为85%,使用BP神经网络分类的正确率达到了91%。

Pixel-level skin segmentation and face color grading

Skin is the largest organ in the human body, and skin color is more convenient and stable than other biological properties of the human body. Therefore, it is very meaningful to design an effective skin color grading system. In this paper, the skin color grading system is divided into two parts: skin segmentation and skin color grading. For the skin segmentation,a multi-scale feature fusion network is built under the framework of the generative adversarial network. Compared with the traditional semantic segmentation networks, the proposed segmentation model makes full use of the information of each layer's feature map. In the face color grading experiment, the SVM classifier and the BP neural network are trained with 1 000 images in the normalized rgb, HSV, and Lab color spaces. 128 skin images are used as test sets, and the correct rate is between 73% and 76%. Then,the color features are combined with the LBP texture features of the skin region to do the learning. The correct rate of the SVM classifier is 85%, and the correct rate of the BP neural network is 91%.