Skin Colour Segmentation Using Finite Bivariate Pearsonian Type-Iib Mixture Model and K-Means

Jagadesh, B. N.

doi:10.5121/sipij.2012.3404

Cited by 4 publications

(2 citation statements)

References 14 publications

(11 reference statements)

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“…Very little work has been reported in skin colour segmentation methods using bivariate Pearsonian mixture models except the works of Jagadesh et al (2012aJagadesh et al ( , 2012b and Srinivasa Rao et al (2012). In these papers, the authors have considered that the image consisting of either African/Asian/European only but not a mixture of all these three races.…”

Section: Introductionmentioning

confidence: 99%

A unified approach for skin colour segmentation using generic bivariate Pearson mixture model

Jagadesh

Rao

Satyanarayana³

2020

IJAIP

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Skin colour segmentation is rapidly growing area of research in computer science for identification and authentication of persons. In this paper, a novel generic bivariate Pearsonian mixture model for skin colour segmentation is proposed. It is observed that the hue and saturation of the colour image better characterise the features of the individual human races. In general, the human race can be characterised in to three categories namely Asian, African and European. The feature of the skin colour of these races are modelled by three different bivariate Pearsonian distributions. The combination of all these three races of people in an image can be characterised by a three component mixture model. Deriving the updated equations of the EM-algorithm, the generic bivariate Pearson mixture model parameters are estimated. The initialisation of the model parameters are done through moment method of estimation and K-means algorithm. The segmentation algorithm is developed using component maximum likelihood under Bayesian frame. The performance of the proposed algorithm is carried by experimentation with random sample of five images and computing the segmentation performance metrics such as PRI, GCE and VOI. The efficiency of the proposed model with that of bivariate GMM is carried through confusion matrix and ROC curves. It is observed that the proposed algorithm outperforms the existing algorithms.

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Section: Introductionmentioning

confidence: 99%

A unified approach for skin colour segmentation using generic bivariate Pearson mixture model

Jagadesh

Rao

Satyanarayana³

2020

IJAIP

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“…They made a comparison between three color spaces (HSV, HSL, and HTS) and found that the last one gave better results than the others. Jagadesh et al [7] presented an approach for skin segmentation using the bivariate Pearsonian Type-IIb Mixture Model. They used the hue and saturation components of HSV color space to distinguish the skin and non-skin pixels, which they based on the threshold values and the Likelihood method, to enhance the accuracy of the results.…”

Section: Related Workmentioning

confidence: 99%

Skin Segmentation Using YUV and RGB Color Spaces

Al-Tairi¹,

Rahmat²,

Saripan³

et al. 2014

Journal of Information Processing Systems

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Abstract-Skin detection is used in many applications, such as face recognition, hand tracking, and human-computer interaction. There are many skin color detection algorithms that are used to extract human skin color regions that are based on the thresholding technique since it is simple and fast for computation. The efficiency of each color space depends on its robustness to the change in lighting and the ability to distinguish skin color pixels in images that have a complex background. For more accurate skin detection, we are proposing a new threshold based on RGB and YUV color spaces. The proposed approach starts by converting the RGB color space to the YUV color model. Then it separates the Y channel, which represents the intensity of the color model from the U and V channels to eliminate the effects of luminance. After that the threshold values are selected based on the testing of the boundary of skin colors with the help of the color histogram. Finally, the threshold was applied to the input image to extract skin parts. The detected skin regions were quantitatively compared to the actual skin parts in the input images to measure the accuracy and to compare the results of our threshold to the results of other's thresholds to prove the efficiency of our approach. The results of the experiment show that the proposed threshold is more robust in terms of dealing with the complex background and light conditions than others.

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