基于CNN深度学习算法的面部毛孔辅助检测系统

Chiun-Li Chin, Zih-Yi Yang, Rui-Cih Su, Cheng-Shiun Yang
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引用次数: 4

摘要

很多人都很关心面部皮肤的保养。毛孔粗大是困扰许多人的面部皮肤问题之一。面部毛孔大小很小,形状各异。因此,使用传统的图像处理方法难以识别面部毛孔。在本文中,我们提出了一种基于卷积神经网络(cnn)的面部毛孔辅助检测系统。我们使用LeNet−5模型作为基准架构,并在我们的面部孔隙数据集上研究了不同深度网络的性能。面部毛孔辅助检测系统可以帮助人们更好地了解自己的面部皮肤问题,更好地保养自己的面部皮肤。
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A Facial Pore Aided Detection System Using CNN Deep Learning Algorithm
Many people are concerned about their facial skin maintenance. Rough pore is one of the facial skin problems which annoyed many people. The size of facial pore is tiny, and it has various shapes. Therefore, it is difficult to recognize facial pore by using traditional image processing. In this paper, we propose an approach based on convolutional neural networks (CNNs) to develop a facial pore aided detection system. We use the LeNet−5 model as our benchmark architecture, and investigate the performance of different depths network on our facial pore dataset. The facial pore aided detection system will help people understand more about their facial skin problems and properly keep their facial skin well.
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