Semi-automatic Labeling and Training Strategy for Deep Learning-based Facial Wrinkle Detection

Kim, Semin; Yoon, Huisu; Lee, Jong-Ha; Yoo, Sangwook

doi:10.1109/cbms55023.2022.00075

Cited by 13 publications

(19 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The advent of deep learning has brought significant performance improvements in facial skin analysis as well. In terms of segmentation tasks, Convolutional Neural Network (CNN) based approaches allow for accurate segmentation of target attributes when provided with the entire face image as input under various shooting conditions 16,17 . The advantages mentioned above make deep learning‐based methods significantly more convenient and robust compared to conventional image processing techniques, which necessitated the application of various filters, parameter optimization, and appropriate ROI selection tailored to each target image 18–21 …”

Section: Methodsmentioning

confidence: 99%

“…In this study, we utilized U‐Net‐based segmentation networks in LUMINI Kiosk V2® that have been adapted and optimized for skin feature segmentation 16,17,22 . To concurrently analyze multiple skin components, a modified form of U‐net with reduced dimensions was employed.…”

Section: Methodsmentioning

confidence: 99%

“…In terms of segmentation tasks, Convolutional Neural Network (CNN) based approaches allow for accurate segmentation of target attributes when provided with the entire face image as input under various shooting conditions. 16,17 The advantages mentioned above make deep learning-based methods significantly more convenient and robust compared to conventional image processing techniques, which necessitated the application of various filters, parameter optimization, and appropriate ROI selection tailored to each target image. [18][19][20][21] In this study, we utilized U-Net-based segmentation networks in LUMINI Kiosk V2® that have been adapted and optimized for skin feature segmentation.…”

Section: Skin Image Analyzermentioning

confidence: 99%

“…[18][19][20][21] In this study, we utilized U-Net-based segmentation networks in LUMINI Kiosk V2® that have been adapted and optimized for skin feature segmentation. 16,17,22 To concurrently analyze multiple skin components, a modified form of U-net with reduced dimensions was employed. Specifically, the channel count of each block was reduced by 1/4, resulting in a model with a 1/16 smaller memory size.…”

Section: Skin Image Analyzermentioning

confidence: 99%

See 3 more Smart Citations

Deep learning‐based skin care product recommendation: A focus on cosmetic ingredient analysis and facial skin conditions

Lee,

Yoon,

Kim

et al. 2024

J of Cosmetic Dermatology

Self Cite

View full text Add to dashboard Cite

BackgroundRecommendations for cosmetics are gaining popularity, but they are not being made with consideration of the analysis of cosmetic ingredients, which customers consider important when selecting cosmetics.AimsThis article aims to propose a method for estimating the efficacy of cosmetics based on their ingredients and introduces a system that recommends personalized products for consumers, combined with AI skin analysis.MethodsWe constructed a deep neural network architecture to analyze sequentially arranged cosmetic ingredients in the product and incorporated skin analysis models to get the precise skin status of users from frontal face images. Our recommendation system makes decisions based on the results optimized for the individual.ResultsOur cosmetic recommendation system has shown its effectiveness through reliable evaluation metrics, and numerous examples have demonstrated its ability to make reasonable recommendations for various skin problems.ConclusionThe result shows that deep learning methods can be used to predict the effects of products based on their cosmetic ingredients and are available for use in personalized cosmetic recommendations.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Skin Image Analyzermentioning

confidence: 99%

Section: Skin Image Analyzermentioning

confidence: 99%

See 2 more Smart Citations

Deep learning‐based skin care product recommendation: A focus on cosmetic ingredient analysis and facial skin conditions

Lee,

Yoon,

Kim

et al. 2024

J of Cosmetic Dermatology

Self Cite

View full text Add to dashboard Cite

show abstract

“…Junayed et al [11] also proposed a dual encoder based on CNN and Transformer, but they detected acne through a semantic segmentation approach. Kim et al [12] enhanced the segmentation performance of acne by training on the positional information of acne in the final encoder.…”

Section: Introductionmentioning

confidence: 99%

Semi-Supervised Facial Acne Segmentation Using Bidirectional Copy-Paste

Kim,

Yoon,

Lee

2024

Preprint

View full text Add to dashboard Cite

Facial acne is a prevalent dermatological condition regularly observed in the general population. This study introduces a novel deep learning model for facial acne segmentation utilizing a semi-supervised learning method known as bidirectional copy-paste, which synthesizes images by interchanging foreground and background parts between labeled and unlabeled images during the training phase. To overcome the lower performance observed in the labeled image training part compared to the previous methods, a new framework was devised to directly compute the training loss based on labeled images. The effectiveness of the proposed method was evaluated against previous semi-supervised learning methods using images cropped from facial images at acne sites. The proposed method achieved a Dice score of 0.5205 in experiments utilizing only 3% of labels, marking an improvement of 0.0151 to 0.0473 Dice score over previous methods. The proposed semi-supervised learning approach for facial acne segmentation demonstrates improvement in performance, offering a novel direction for future acne analysis.

show abstract

Artificial Intelligence Approaches for Skin Anti-aging and Skin Resilience Research

Georgievskaya¹,

Danko²,

Baxter³

et al. 2023

Healthy Ageing and Longevity

View full text Add to dashboard Cite

Semi-automatic Labeling and Training Strategy for Deep Learning-based Facial Wrinkle Detection

Cited by 13 publications

References 11 publications

Deep learning‐based skin care product recommendation: A focus on cosmetic ingredient analysis and facial skin conditions

Deep learning‐based skin care product recommendation: A focus on cosmetic ingredient analysis and facial skin conditions

Semi-Supervised Facial Acne Segmentation Using Bidirectional Copy-Paste

Artificial Intelligence Approaches for Skin Anti-aging and Skin Resilience Research

Contact Info

Product

Resources

About