Skin tactile surface restoration using deep learning from a mobile image: An application for virtual skincare

Kim, Yejin; Ko, Myeongseob; Kim, K.

doi:10.1111/srt.13009

Cited by 5 publications

(2 citation statements)

References 35 publications

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“…Another undesirable consequence is the pursuit of, sometimes unrealistic, beauty ideals, a phenomenon that is very difficult to rectify. Even at the risk of their own health, people use products to lighten their skin, color their hair [41,42,44,45] and apply products or treatments to pursuit smooth and uniformly pigmented skin [43,44,46,47]. There is some optimism in this regard, as in recent years the universality of beauty standards is being replaced by features specific to different ethnicities and cultures [43,47,48].…”

Section: Beauty Definition Biasmentioning

confidence: 99%

How artificial intelligence adopts human biases: the case of cosmetic skincare industry

Georgievskaya,

Tlyachev,

Danko

et al. 2023

AI Ethics

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The cosmetic skincare industry is a growing market that extends to different regions and customer groups. In addition to scientific advances and technological developments, state-of-the-art digital approaches, including machine learning and other artificial intelligence (AI)-based techniques, are being applied at different stages of the value chain. The objectives of these efforts include optimizing the supply chain, developing high-quality, effective and safe products and personalization at every step of the customer journey. However, the use of digital technologies comes with risks and undesirable effects. These include a lack of transparency and accountability, compromised fairness and a general deficiency in data governance, all of which are critical at every customer touchpoint. This dark side of digital transformation is recognized by both businesses and governments. In this paper, we explain the concept of bias leading to unfairness for beauty technology applications. Based on published data we identified potential sources of AI bias in the cosmetic skincare industry and/or beauty tech. They were classified by the stage of the AI lifecycle: biases related to target setting, to acquisition and annotation, to modeling, to validation and evaluation, and to deployment and monitoring. We aim to create awareness of such phenomena among readers, whether executives, managers, developers or potential end-users.

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Section: Beauty Definition Biasmentioning

confidence: 99%

How artificial intelligence adopts human biases: the case of cosmetic skincare industry

Georgievskaya,

Tlyachev,

Danko

et al. 2023

AI Ethics

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show abstract

“…Besides, DL also has good migration ability and can be used to solve the problem of improving the resolution of complex tongue tactile imaging. Deep neural networks, such as deconvolution neural networks (DNNs), have shown great potential in improving the resolution of tactile perception imaging (Funabashi et al, 2022; Huang et al, 2020; Kim et al, 2021; Quqa et al, 2022). It can efficiently transform the distributed information of sparse original pressure contacts into high‐resolution tactile image close to tongue tactile perception.…”

Section: Introductionmentioning

confidence: 99%

Resolution enhancement of tongue tactile image based on deconvolution neural network

Liu

Shen

Zhao

et al. 2023

Journal of Texture Studies

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To reproduce the tactile perception of multiple contacts on the human tongue surface, it is necessary to use a pressure measurement device with high spatial resolution. However, reducing the size of the array sensing unit and optimizing the lead arrangement still pose challenges. This article describes a deconvolution neural network (DNN) for improving the resolution of tongue surface tactile imaging, which alleviates this tradeoff between tactile sensing performance and hardware simplicity.The model can work without high-resolution tactile imaging data of tongue surface: First, in the compression test using artificial tongues, the tactile image matrix (7 Â 7) with low resolution can be acquired by sensor array with a sparse electrode arrangement. Then, through finite element analysis modeling, combined with the distribution rule of additional stress on the two-dimensional plane, the pressure data around the existing detection points are calculated, further expanding the tactile image matrix data amount. Finally, the DNN, based on its efficient nonlinear reconstruction attributes, uses the low-resolution and high-resolution tactile imaging matrix generated by compression test and finite element simulation, respectively, to train, and outputs high-resolution tactile imaging information (13 Â 13) closer to the tactile perception of the tongue surface. The results show that the overall accuracy of the tactile image matrix calculated by this model is above 88%. Then, we deduced the spatial difference graph of the resilience index of the three kinds of ham sausages through the high-resolution tactile imaging matrix.

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Image Based Virtual Reality Haptic Simulation for Multimodal Skin Tumor Surgery Training

Kim

Jeong

Kim

et al. 2021

2021 4th International Conference on Bio-Engineering for Smart Technologies (BioSMART)

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Skin tactile surface restoration using deep learning from a mobile image: An application for virtual skincare

Cited by 5 publications

References 35 publications

How artificial intelligence adopts human biases: the case of cosmetic skincare industry

How artificial intelligence adopts human biases: the case of cosmetic skincare industry

Resolution enhancement of tongue tactile image based on deconvolution neural network

Image Based Virtual Reality Haptic Simulation for Multimodal Skin Tumor Surgery Training

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