Human skin detection through correlation rules between the YCb and YCr subspaces based on dynamic color clustering

Pietro, Giuseppe De; Frucci, Maria; Gallo, Luigi

doi:10.1016/j.cviu.2016.12.001

Cited by 45 publications

(62 citation statements)

References 25 publications

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“…8 shows that few of the statistical methods (adaptive Bayesian, Bayesian2, and product) have a lower FPR than the proposed one. However, the benefit comes with a trade off in accuracy.…”

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confidence: 88%

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Skin detection in image and video founded in clustering and region growing

Islam¹

2019

Mobile Multimedia/Image Processing, Security, and Applications 2019

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Secondly, I am grateful to all faculty and staff of the Computer Science and Engineering Department for their consistent support and willingness to answer all my questions. Specifically, I want to express my most sincere gratitude to my advisor Dr. Bill Buckles for guidance and patience. His kind and helpful supervision throughout the process were beyond my expectations, and I am deeply honored to have worked with him as a student. I believe I received immeasurable support from him, and I wish him and his lovely family good health and prosperity.

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“…8 shows that few of the statistical methods (adaptive Bayesian, Bayesian2, and product) have a lower FPR than the proposed one. However, the benefit comes with a trade off in accuracy.…”

mentioning

confidence: 88%

“…Bran [8] presents a pixel-based skin detection using the YCbCr color space. This approach takes advantage of combination of chrominance values for YCb and YCr color subspaces for skin detection.…”

Section: Skin Detection For Hgr Datasetmentioning

confidence: 99%

Skin detection in image and video founded in clustering and region growing

Islam¹

2019

Mobile Multimedia/Image Processing, Security, and Applications 2019

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“…Machine learning and hybrid methods require a training set, from which the partition configuration is learned. In general, such approaches outperform the rulebased methods but require a large and representative training dataset as well as it takes a long training time and, regularly, a costly classification time as well, which can be a deal breaker for real-time applications (Brancati et al, 2017;Kakumanu et al, 2007).…”

Section: 3mentioning

confidence: 99%

“…In this work, we propose an improvement of a novel method for rule-based skin detection that works in the YCbCr color space (Brancati et al, 2017). Our motivation is based on the hypothesis that the original rule can be complemented by another rule that is a reversal interpretation of the one originally proposed.…”

Section: 3mentioning

confidence: 99%

“…Besides that, we also take into consideration that a skin pixel does not appear isolated, so we propose another variation based on neighborhood operations. The set of rules evaluate the combinations of chrominance Cb, Cr values to identify the skin pixels depending on the shape and size of dynamically generated skin color clusters (Brancati et al, 2017). The method is very efficient in terms of computational effort as well as robust in very complex images (i.e.…”

Section: 3mentioning

confidence: 99%

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Human skin segmentation using correlation rules on dynamic color clustering

Faria¹

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Human skin is made of a stack of different layers, each of which reflects a portion of impinging light, after absorbing a certain amount of it by the pigments which lie in the layer. The main pigments responsible for skin color origins are melanin and hemoglobin. Skin segmentation plays an important role in a wide range of image processing and computer vision applications. In short, there are three major approaches for skin segmentation: rule-based, machine learning and hybrid. They differ in terms of accuracy and computational efficiency. Generally, machine learning and hybrid approaches outperform the rule-based methods but require a large and representative training dataset and, sometimes, costly classification time as well, which can be a deal breaker for real-time applications. In this work, we propose an improvement, in three distinct versions, of a novel method for rule-based skin segmentation that works in the YCbCr color space. Our motivation is based on the hypotheses that: (1) the original rule can be complemented and, (2) human skin pixels do not appear isolated, i.e. neighborhood operations are taken into consideration. The method is a combination of some correlation rules based on these hypotheses. Such rules evaluate the combinations of chrominance Cb, Cr values to identify the skin pixels depending on the shape and size of dynamically generated skin color clusters. The method is very efficient in terms of computational effort as well as robust in very complex images.

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Development of an image‐based measurement system for human facial skin colour

Xiao

Pointer

et al. 2021

Color Research & Application

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In this study, an image‐based measurement system was developed for human facial skin colour, involving the development of a digital imaging system, collection of facial skin colour from 60 human subjects, generation of different colour characterization models, and performance evaluation. The factors that affect facial skin colour characterization, including different training datasets (two colour charts and the collected facial skin colour dataset), mathematical mapping methods (linear transformation, polynomial regression, root‐polynomial regression and neural network) and camera image formats (JPG and RAW), were investigated and quantified not only by the conventional method of CIELAB colour difference, but also two newly introduced measures, facial colour contrast and skin colour gamut. The results indicate that the RAW image format for camera digital signals gave a more stable performance than the JPG format images, and the higher order polynomial regression with good predictive accuracy in terms of CIELAB colour difference did not perform well for the whole facial image. It is suggested to evaluate the model performance using the colour of both specific facial positions and the overall facial skin colour. Our comparative analysis in this study provides useful guidance for determining the colour characterization model for facial skin.

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Human skin detection through correlation rules between the YCb and YCr subspaces based on dynamic color clustering

Cited by 45 publications

References 25 publications

Skin detection in image and video founded in clustering and region growing

Skin detection in image and video founded in clustering and region growing

Human skin segmentation using correlation rules on dynamic color clustering

Development of an image‐based measurement system for human facial skin colour

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