卷积神经网络(CNN)深度学习方法在女性皮肤分类中的应用

A. Anton, Novia Farhan Nissa, Angelia Janiati, Nilam Cahya, P. Astuti
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引用次数: 23

摘要

面部皮肤是保护面部内部的皮肤,如眼睛、鼻子、嘴巴和其他部位。面部皮肤由几种类型组成,包括正常皮肤、油性皮肤、干性皮肤和混合性皮肤。这对女性来说是一个问题,因为很难识别和区分自己的皮肤类型——这就是为什么一些女性很难确定适合自己皮肤类型的化妆品和护理产品。在本研究中,卷积神经网络(CNN)方法是使用深度为三层的Python 3.5程序分几个阶段对20-30岁女性皮肤类型进行分类的正确方法,使用CNN方法进行的研究结果的准确度值良好,达到67%
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Application of Deep Learning Using Convolutional Neural Network (CNN) Method For Women’s Skin Classification
Facial skin is skin that protects the inside of the face such as the eyes, nose, mouth, and others. Facial skin consists of several types, including normal skin, oily skin, dry skin, and combination skin. This is a problem for women because it is difficult to recognize and distinguish their skin types this is what causes some women to find it difficult to determine the right make-up and care products for their skin types. In this study, the Convolutional Neural Network (CNN) method is the right method for classifying women's skin types from the age of 20-30 years by following several stages using Python 3.5 programming with a depth of three layers and the results of this research using the CNN method get the results of the accuracy value good at 67%
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