A comparative assessment of three approaches to pixel-level human skin-detection

Brand, J.D.; Mason, John S.

doi:10.1109/icpr.2000.905653

Cited by 157 publications

(85 citation statements)

References 9 publications

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“…It has been shown that RGB, YCbCr and HSV have similar performance while CbCr is different since being a 'non-invertible' color transformation. The skin probability Map (SPM) with RGB color space is reported as the best combination in [3], compared to Red-Green (rg) ratio and linear color transformation of RGB color space into YIQ color space.…”

Section: Related Workmentioning

confidence: 99%

Color based skin classification

Khan

Hanbury

Stöttinger

et al. 2012

Pattern Recognition Letters

133

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Skin detection is used in applications ranging from face detection, tracking body parts and hand gesture analysis, to retrieval and blocking objectionable content. In this paper, we investigate and evaluate (1) the effect of color space transformation on skin detection performance and finding the appropriate color space for skin detection, (2) the role of the illuminance component of a color space, (3) the appropriate pixel based skin color modeling technique and finally, (4) the effect of color constancy algorithms on color based skin classification. The comprehensive color space and skin color modeling evaluation will help in the selection of the best combinations for skin detection. Nine skin modeling approaches (AdaBoost, Bayesian network, J48, Multilayer Perceptron, Naive Bayesian, Random Forest, RBF network, SVM and the histogram approach of Jones and Rehg [15]) in six color spaces (IHLS, HSI, RGB, normalized RGB, YCbCr and CIELAB) with the presence or absence of the illuminance component are compared and evaluated. Moreover, the impact of five color constancy algorithms on skin detection is reported. Results on a database of 8991 images with manually annotated pixel-level ground truth show that (1) the cylindrical color spaces outperform other color spaces, (2) the absence of the illuminance component decreases performance, (3) the selection of an appropriate skin color modeling approach is important and that the tree based classifiers (Random forest, J48) are well suited to pixel based skin detection. As a best combination, the Random Forest combined with the cylindrical color spaces, while keeping the illumi- nance component outperforms other combinations, and (4) the usage of color constancy algorithms can improve skin detection performance.

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Section: Related Workmentioning

confidence: 99%

Color based skin classification

Khan

Hanbury

Stöttinger

et al. 2012

Pattern Recognition Letters

133

View full text Add to dashboard Cite

show abstract

“…Specifically, the R/G ratio of the intensity value on the red (R) channel to the intensity on the green (G) channel can be used to represent skin [32,33]. Using the position of the dot to get a sample of the skin hue on the forehead, it is thus possible to detect skin on the face of a specific patient.…”

Section: Skin Recognitionmentioning

confidence: 99%

“…Using the position of the dot to get a sample of the skin hue on the forehead, it is thus possible to detect skin on the face of a specific patient. Note that the studies of [32,33] successfully detect skin on a very large data set containing a wide number of skin types. In critical care patients there is the possibility of paler skin due to their severe illness.…”

Section: Skin Recognitionmentioning

confidence: 99%

Measuring facial grimacing for quantifying patient agitation in critical care

Becouze

Hann

Chase

2007

Computer Methods and Programs in Biomedicine

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The effective delivery of sedation in critical care relies primarily on an accurate and consistent measure of a patient's agitation level. However, current methods for assessing agitation are subjective and prone to error, often leading to over sedation or cycles between agitation and oversedation. This paper builds on previous work developing agitation sensors based on heart rate and blood pressure variability, and overall whole body motion. In this research, the focus is on real-time measurement of high resolution facial changes that are observed to occur in agitation. An algorithm is developed that measures the degree of facial grimacing from a single digital camera.The method is demonstrated on simulated patient facial motion to prove the concept.A consistent measure is obtained that is robust to significant random head movement and compares well against visual observation of different levels of grimacing. The method provides a basis for clinical validation.

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“…The key idea of this method is to estimate skin colour distribution from the training data without having to derive an explicit model of the skin colour. This method is also referred as a SPM (Skin Probability Map) (Brand & Mason 2000, Gomez 2000. By using this method, a skin colour database was established as a training data.…”

Section: Skin Colour Segmentationmentioning

confidence: 99%

Perceptive three dimensional interface via stereo observation

Padbury¹,

McCrindle²,

Wei³

2006

International Journal on Disability and Human Development

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This paper describes an intuitive approach for interacting with a computer or computer-driven applications. Interaction is achieved by observing, through a stereo camera set-up, the motion of a user's hands. This motion is then translated into 3-dimensional (3-D) coordinates to enable interaction with either a traditional 2-dimensional (2-D) desktop or a novel 3-D user interface. The aim of this work is to provide an intuitive method of interaction to computer based applications for individuals whose condition might restrict their ability to use a standard keyboard/mouse.

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A comparative assessment of three approaches to pixel-level human skin-detection

Cited by 157 publications

References 9 publications

Color based skin classification

Color based skin classification

Measuring facial grimacing for quantifying patient agitation in critical care

Perceptive three dimensional interface via stereo observation

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