Skin Visualization Using Smartphone and Deep Learning in the Beauty Industry

Hasegawa, Makoto; Matsuo, Rui

doi:10.1587/transinf.2021edk0004

Cited by 2 publications

(3 citation statements)

References 9 publications

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“…Essay scoring methods based on artificial features and essay scoring methods based on neural networks have different advantages and disadvantages. The former mainly relies on experts to design relevant features of the composition text to represent the composition [8]. This method is interpretable for the details of the scoring.…”

Section: Research On Automatic English Scoring Algorithm Based On Mac...mentioning

confidence: 99%

Research on automatic scoring algorithm for English composition based on machine learning

2024

International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023)

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It is difficult to extract deep semantic features for English composition scoring methods based on artificial features, and it is difficult for English composition scoring methods based on neural networks to extract shallow features such as the number of words, resulting in the limitations of different composition scoring methods. Based on existing research results, this paper proposes an English composition scoring method that combines artificial feature extraction methods and deep learning methods. This method uses artificially designed features to extract shallow features at the word and sentence levels in the composition, draws on existing methods to extract semantic features of the composition, and performs regression calculations on the deep features and shallow features to obtain the total score of the composition. The experiment uses the Pearson evaluation index to measure the correlation between the predicted total score of the essay and the true total score under the combination method. The experiment shows that compared with the average results of 0.747 and 0.645 of baseline models such as BiLSTM and RNN, the algorithm proposed in this article is respectively improvements are 0.068 and 0.17, which proves the effectiveness of the method proposed in this paper.

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Section: Research On Automatic English Scoring Algorithm Based On Mac...mentioning

confidence: 99%

Research on automatic scoring algorithm for English composition based on machine learning

2024

International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023)

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“…introduced a method for capturing macro skin images using a smartphone with a ring light of LEDs, alongside an image conversion process to transform these into pseudo‐UV images using CycleGAN, a deep learning technique. 14 , 15 …”

Section: Introductionmentioning

confidence: 99%

“…13 Hasegawa et al introduced a method for capturing macro skin images using a smartphone with a ring light of LEDs, alongside an image conversion process to transform these into pseudo-UV images using CycleGAN, a deep learning technique. 14,15 These endeavors mark significant progress in evaluating partial facial skin quality using smartphones. However, to achieve comprehensive facial aesthetic monitoring-a prerequisite for accurately gauging perceptions of age and health-a novel approach is imperative to standardize facial appearance conditions in images captured by smartphone cameras.…”

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confidence: 99%

A smartphone application for personalized facial aesthetic monitoring

Hashimoto,

Kaneda

2024

Skin Research and Technology

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BackgroundMethods available at home for capturing facial images to track changes in skin quality and evaluate skincare treatments are limited. In this study, we developed a smartphone camera application (app) for personalized facial aesthetic monitoring.Materials and MethodsA face alignment indicators (FAIN) system utilizing facial landmark detection, an artificial intelligence technique, to estimate key facial parts, was implemented into the app to maintain a consistent facial appearance during image capture. The FAIN system is composed of a fixed target indicator and an alignment indicator that dynamically changes its shape according to the user's face position, size, and orientation. Users align their faces to match the alignment indicator with the fixed target indicator, and the image is automatically captured when alignment is achieved.ResultsWe investigated the app's effectiveness in ensuring a consistent facial appearance by analyzing both geometric and colorimetric data. Geometric information from captured faces and colorimetric data from stickers applied to the faces were utilized. The coefficients of variation (CVs) for the L*, a*, and b* values of the stickers were higher compared to those measured by a colorimeter, with CVs of 14.9 times, 8.14 times, and 4.41 times for L*, a*, and b*, respectively. To assess the feasibility of the app for facial aesthetic monitoring, we tracked changes in pseudo‐skin color on the cheek of a participant using skin‐colored stickers. As a result, we observed the smallest color difference ∆Eab of 1.901, which can be considered as the experimentally validated detection limit using images acquired by the app.ConclusionWhile the current monitoring method is a relative quantification approach, it contributes to evidence‐based evaluations of skincare treatments.

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Skin Visualization Using Smartphone and Deep Learning in the Beauty Industry

Cited by 2 publications

References 9 publications

Research on automatic scoring algorithm for English composition based on machine learning

Research on automatic scoring algorithm for English composition based on machine learning

A smartphone application for personalized facial aesthetic monitoring

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