Maximum Entropy Models for Skin Detection

Jedynak, Bruno; Zheng, Huicheng; Daoudi, Mohamed

doi:10.1007/978-3-540-45063-4_12

Cited by 22 publications

(21 citation statements)

References 19 publications

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“…The works of Yang and Ahuja (Yang and Ahuja, 1998), Kruppa (Kruppa et al, 2002), Jedyank (Jedynak et al, 2003) and Sebe ( Sebe et al, 2004) are the close to ours. However, Yang and Ahuja (Yang and Ahuja, 1998) used multiscale segmentations to find elliptical regions for face detection.…”

Section: Related Worksupporting

confidence: 77%

“…skin color in this case. Also, Jedyank (Jedynak et al, 2003) used hidden Markov model at pixel level, while we use conditional random fields and operate on superpixel, as described in section 3.4.…”

Section: Related Workmentioning

confidence: 99%

“…However, it is natural to treat skin or non-skin as regions instead of individual pixels. Surprisingly, there are only few region-based skin detection techniques (Yang and Ahuja, 1998), (Kruppa et al, 2002), (Jedynak et al, 2003) and (Sebe et al, 2004). Kruppa (Kruppa et al, 2002), Yang and Ahuja (Yang and Ahuja, 1998) searched for elliptical skin color shape to find the face.…”

Section: Introductionmentioning

confidence: 99%

“…Kruppa (Kruppa et al, 2002), Yang and Ahuja (Yang and Ahuja, 1998) searched for elliptical skin color shape to find the face. Sebe (Sebe et al, 2004) used fixed 3x3 pixel patches to train a Bayesian network and Jedyank (Jedynak et al, 2003) smoothed the results using hidden Markov model. This paper proposes a new technique purely based on the concept of regions, irrespective of the underlying geometrical shape.…”

Section: Introductionmentioning

confidence: 99%

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Region-Based Skin Color Detection

Poudel¹,

Nait-Charif²,

Zhang³

et al. 2012

Proceedings of the International Conference on Computer Vision Theory and Applications

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Abstract:Skin color provides a powerful cue for complex computer vision applications. Although skin color detection has been an active research area for decades, the mainstream technology is based on the individual pixels. This paper presents a new region-based technique for skin color detection which outperforms the current state-of-the-art pixel-based skin color detection method on the popular Compaq dataset (Jones and Rehg, 2002). Color and spatial distance based clustering technique is used to extract the regions from the images, also known as superpixels. In the first step, our technique uses the state-of-the-art non-parametric pixel-based skin color classifier (Jones and Rehg, 2002) which we call the basic skin color classifier. The pixel-based skin color evidence is then aggregated to classify the superpixels. Finally, the Conditional Random Field (CRF) is applied to further improve the results. As CRF operates over superpixels, the computational overhead is minimal. Our technique achieves 91.17% true positive rate with 13.12% false negative rate on the Compaq dataset tested over approximately 14,000 web images.

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Section: Related Worksupporting

confidence: 77%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Region-Based Skin Color Detection

Poudel¹,

Nait-Charif²,

Zhang³

et al. 2012

Proceedings of the International Conference on Computer Vision Theory and Applications

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“…A more advance modelling technique employing statistical based approaches such as neural network [26], Bayesian [27], maximum entropy [28] and k-means clustering [29] have also been used to detect skin colour pixel. Many different modelling techniques for discriminating between skin and non-skin regions are available in the literature.…”

Section: Introductionmentioning

confidence: 99%

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2012

IJSC

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Facial Skin Color Modeling

Martinkauppi

Pietikäinen

Handbook of Face Recognition

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Color information has been commonly used to assist face detection. Its popularity can be attributed to several factors such as computational efficiency and discrimination power. However, because of its sensitivity to illumination variations, changes in illumination (especially in chromaticity) can be detrimental to some models.Use of color in facial skin detection may not be enough: It cannot necessarily distinguish faces from other objects (e.g., hands, wood) with a similar appearance. Other cues are therefore needed to verify that the selected area is indeed a face. Color is, however, still useful as a preprocessing step because it may significantly reduce the number of candidates by eliminating obviously false targets.In this chapter, we try to answer the following questions: What factors should be taken into account when selecting a skin color model? How can we find a suitable model?The chapter is organized in the following way: Section 1 provides an introduction to the problem. In Section 2, the behavior of skin color in different color spaces is considered. Section 3 presents a review of skin color models, and Section 4 describes the skin locus approach and compares it to other methods. Finally, discussion is presented in Section 5.

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Maximum Entropy Models for Skin Detection

Cited by 22 publications

References 19 publications

Region-Based Skin Color Detection

Region-Based Skin Color Detection

Untitled

Facial Skin Color Modeling

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