Automatic Skin Tone Extraction for Visagism Applications

Borza, Diana; Darabant, Adrian Sergiu; Danescu, Radu

doi:10.5220/0006711104660473

Cited by 5 publications

(10 citation statements)

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“…Public datasets prove valuable for CNN model training, addressing the challenge of data collection. However, certain prior studies [5,7] predominantly featured images under diverse lighting conditions, with limited variation in skin tones. This consideration prompts the authors of this project to explore data collection methods, drawing inspiration from [20] for image acquisition as a foundational framework.…”

Section: Data Pre-processing Methodsmentioning

confidence: 99%

“…Borza et al developed a method to aid with the selection of eyeglasses inspired by recent fashion trends, where choosing the right eyeglass frame is crucial. The selection process involved various factors, such as face shape, skin tone, and eye colour, which can be challenging and costly when conducted by human analysts [7]. The training dataset was created by combining facial images from various face databases, including the Caltech [21] dataset, the Chicago Face dataset [22], the Minear-Park database, and the Brazilian face database, with the categorisation based on subjects' skin colours.…”

Section: Related Workmentioning

confidence: 99%

“…The accurate estimation of human skin tone from colour images is important in diverse applications that rely on precise colour representation in visual data. This research topic is closely intertwined with the pursuit of colour accuracy and colour constancy, affecting technologies such as facial recognition [1][2][3], medical imaging [4][5][6], and the cosmetic and beauty industry [7]. In these domains, the quality and correctness of outcomes are intrinsically tied to the faithful recovery of an individual's skin tone.…”

Section: Introductionmentioning

confidence: 99%

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Skin Tone Estimation under Diverse Lighting Conditions

Mbatha,

Booysen,

Theart

2024

J. Imaging

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Knowledge of a person’s level of skin pigmentation, or so-called “skin tone”, has proven to be an important building block in improving the performance and fairness of various applications that rely on computer vision. These include medical diagnosis of skin conditions, cosmetic and skincare support, and face recognition, especially for darker skin tones. However, the perception of skin tone, whether by the human eye or by an optoelectronic sensor, uses the reflection of light from the skin. The source of this light, or illumination, affects the skin tone that is perceived. This study aims to refine and assess a convolutional neural network-based skin tone estimation model that provides consistent accuracy across different skin tones under various lighting conditions. The 10-point Monk Skin Tone Scale was used to represent the skin tone spectrum. A dataset of 21,375 images was captured from volunteers across the pigmentation spectrum. Experimental results show that a regression model outperforms other models, with an estimated-to-target distance of 0.5. Using a threshold estimated-to-target skin tone distance of 2 for all lights results in average accuracy values of 85.45% and 97.16%. With the Monk Skin Tone Scale segmented into three groups, the lighter exhibits strong accuracy, the middle displays lower accuracy, and the dark falls between the two. The overall skin tone estimation achieves average error distances in the LAB space of 16.40±20.62.

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Section: Data Pre-processing Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Skin Tone Estimation under Diverse Lighting Conditions

Mbatha,

Booysen,

Theart

2024

J. Imaging

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“…The SVM is a binary linear classification model that determines the category to which a given dataset belongs. In this study, a one‐to‐one approach was used for classification into five categories, and a radial basis function kernel was used 18

\begin{equation}K\left( {x,\;x^{\prime}} \right) = {\bf{exp}}\left( {\frac{{x - {{x^{\prime}}^2}}}{{2{\sigma ^2}}}} \right)\end{equation}

…”

Section: Methodsmentioning

confidence: 99%

Skin color classification of Koreans using clustering

Kye

Lee

2022

Skin Research and Technology

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Background/purpose: Skin color is used as an index for diagnosing and predicting skin irritation, dermatitis, and skin conditions because skin color changes based on various factors. Therefore, a new method for consistently and accurately evaluating skin color while overcoming the limitations of the existing skin color evaluation method was proposed, and its usefulness was demonstrated.Methods: Skin color was quantified using the RGB (Red, Green, Blue), HSV (Hue Saturation Value), CIELab, and YCbCr color spaces in the acquired Korean skin images, which were classified through clustering. In addition, the classification performances of the existing visual scoring method and the proposed skin color classification method were compared and analyzed using multinomial logistic regression, support vector machine, K-nearest neighbor, and random forest.Results: After quantifying the skin color through the color space conversion of the skin image, the skin color classification performance according to the number of quantified features and the classifier was verified. In addition, the usefulness of the proposed classification method was confirmed by comparing its classification performance with that of the existing skin color classification method. Conclusion:In this study, a method was proposed to objectively classify skin color values quantified from skin images of Koreans acquired using a digital camera through clustering. To verify the proposed method, its classification performance was compared with that of the existing classification method, and an optimized classification method was presented for the classification of Korean skin color. Thus, the proposed method can objectively classify skin color and can be used as a cornerstone in research to quantify skin color and establish objective classification criteria.

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“…The skin color of each region can also be determined with SVM and CNN models. Automatic analysis modules have been developed, especially in optical visagism systems through the use of skin color by eyewear virtual try-on software via a mobile device (Borza et al, 2018).…”

Section: Prosthodonticsmentioning

confidence: 99%

Artificial Intelligence Technologies in Dentistry

Albayrak¹,

Ozdemir²,

Us³

et al. 2021

J Exp Clin Med

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One of the most important actors in the digitization process of our age has been the applications of artificial intelligence (AI). While the weak and strong AI sub-concepts and the different AI models within them are being utilized in many fields such as education, industry and medicine today, the interest of the dentistry field, which has started its integration into the digital world with CAD/CAM technology, in AI is increasing day by day. In different branches of dentistry; AI provides services to clinicians and researchers in many fields such as disease diagnosis, evaluation of the occurrence or recurrence of diseases such as oral cancer, and prediction of success in surgical and prosthetic treatments. In this article, studies in which AI models such as machine learning, convolutional neural network have found research and usage areas on the basis of different branches of dentistry are reviewed.

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Automatic Skin Tone Extraction for Visagism Applications

Cited by 5 publications

References 0 publications

Skin Tone Estimation under Diverse Lighting Conditions

Skin Tone Estimation under Diverse Lighting Conditions

Skin color classification of Koreans using clustering

Artificial Intelligence Technologies in Dentistry

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