Deep Scattering Convolutional Network for Cosmetic Skin Classification

Deep Scattering Convolutional Network for Cosmetic Skin Classification

© 2022 by IJETT Journal
Volume-70 Issue-7
Year of Publication : 2022
Authors : Shwetambari Borade, Dhananjay Kalbande, Kristen Pereira, Rushil Patel, Sudhanshu Kulkarni
DOI : 10.14445/22315381/IJETT-V70I7P202

How to Cite?

Shwetambari Borade, Dhananjay Kalbande, Kristen Pereira, Rushil Patel, Sudhanshu Kulkarni, "Deep Scattering Convolutional Network for Cosmetic Skin Classification" International Journal of Engineering Trends and Technology, vol. 70, no. 7, pp. 10-23, 2022. Crossref,

The use of computer-based solutions or artificial intelligence in detecting face skin conditions has progressed considerably over the years. Combining cosmetic skin care concepts with technical advancements has shown noble outputs. In recent times, deep convolutional neural networks have been applied for image classification applications in various domains like healthcare, identification of objects in various aspects, control systems, machine visions, etc., and have shown remarkable results. The scattering network is commonly and consistently utilized on the initial layers of a supervised mixed convolutional model. This study demonstrates that layers in the first layer do not always have to be learned, with pre-defined models attaining the greatest performance to date when competing with Deep CNNs. ResNETs, in particular, have been adopted widely because they can solve the vanishing gradient problem that prevails in most deep networks. So, they have exploited the capacity of wavelet transforms, Scattering Networks, and the Residual Networks model for image classification to identify cosmetic skin problems like oily or dry skin. According to results obtained and statistical assessments, the ResNet model and Scattering Networks have the greatest validation accuracy of 98 percent for image classification of oily and dry skin pictures. They have also compared the model with others where scattering networks for SVM have an accuracy of 57 percent, whereas VGG16 gave an accuracy of 95.9 percent on validation sets. And DWT with ANN has an accuracy of 63.2 percent, and Gabor filters and support vector machine have the lowest accuracy of 48.8 percent.

Deep Learning, Histogram Equalization, ResNet, Skin Classification, Wavelet Scattering.

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