使用深度学习实时检测各种衰老迹象

A. Sameera, V. Samuktha, T. Akash, M. Sabeshnav, S. Veni
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引用次数: 1

摘要

深度学习技术和CNN最有前途的领域之一是化妆品和皮肤科行业。通过面部检测和识别等深度学习程序,可以很容易地检测出早衰等情况。这个项目主要是基于改进这些领域的技术。利用cnn建立了一个深度学习模型,该网络配备了手工制作的特征,如皱纹、痤疮和瑕疵。该模型将能够同时区分这些特征,并具有多种应用。与以前的模型相比,它的计算效率很高,并且使用了特殊的卷积和池化操作,并进行了参数移位。总体准确率达到94.11%。
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Real Time Detection of the Various Sign of Ageing Using Deep Learning
One of the most promising fields where the technology of deep learning and CNN can thrive are the cosmetic and dermatology industries. Detection of conditions like premature ageing can be made easy by deep learning procedures like facial detection and recognition. This project is based on improving the technology principally in these domains. A deep learning model utilizing CNNs is built, and the network is equipped with hand-crafted characteristics like wrinkles, acne and blemishes. The model will be able to distinguish these features concurrently and has diverse applications. It is computationally efficient compared to previous models, and it uses special convolution and pooling operations and performs parameter shifting. An overall accuracy of 94.11 % was achieved.
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