New color transformation for lips segmentation

Eveno, N.; Caplier, Alice; Coulon, Pierre-Yves

doi:10.1109/mmsp.2001.962702

Cited by 71 publications

(44 citation statements)

References 6 publications

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“…For both, red is prevalent. Moreover there is more green than blue in the skin color mixture and for lips both components are almost the same [7]. Skin appears more yellow than lips because the difference between red and green is greater for lips than for skin.…”

Section: Skin and Lips Color Analysismentioning

confidence: 95%

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Parametric models for facial features segmentation

et al. 2006

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-In this paper, we are dealing with the problem of facial features segmentation (mouth, eyes and eyebrows). A specific parametric model is defined for each deformable feature, each model being able to take into account all the possible deformations. In order to initialize each model, some characteristic points are extracted on each image to be processed (for example, eyes corners, mouth corners and brows corners). In order to fit the model with the contours to be extracted, a gradient flow (of luminance or chrominance) through the estimated contour is maximized because at each point of the searched contour, the gradient (of luminance or chrominance) is normal. The definition of a model associated to each feature offers the possibility to introduce a regularisation constraint. However, the chosen models are flexible enough to produce realistic contours for the mouth, the eyes and the eyebrows. This facial features segmentation is the first step of a set of multi-media applications.

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Section: Skin and Lips Color Analysismentioning

confidence: 95%

“…Unlike usual hue, the pseudo hue is bijective. It is higher for lips than for skin [7] (cf. Figure 13).…”

Section: R X Y H X Y G X Y R X Y = +mentioning

confidence: 99%

Parametric models for facial features segmentation

et al. 2006

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“…Their purpose is to transform the colour space to maximize the chromatic difference between skin and lips that led to use based on the connected between the R and G components of the RGB system. The transform is combined with pixel-based classification by [62].In 63], used to transform the information on the RGB colour under the CIELUV space. The adaptive used histogram-based operation thresholds later resulted in a binary classifier for lip segmentation.…”

Section: Lips Transformationmentioning

confidence: 99%

4D Automatic Lip-Reading for Speaker's Face Identification

AbUShana¹

2018

SIPIJ

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“…The brightness of human skin/lips can vary significantly with differing lighting conditions but is found to have consistent chromatic features [7]. Many approaches therefore transform the RGB signal to discard the luminance component, utilising chrominance components in the segmentation [3][4] [7][8] [9].…”

Section: Colour Based Segmentation Of Mouth Roimentioning

confidence: 99%

Region of interest extraction using colour based methods on the CUAVE Database

Berry

Harte

2009

IET Irish Signals and Systems Conference (ISSC 2009)

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-Region of interest (ROI) extraction is an important step in deriving visual features for an audio-visual speech recognition system. Colour based segmentation offers the potential of computationally inexpensive algorithms for ROI selection. This paper presents a comparative study of two colour based techniques, one using hue and accumulated difference, the other chrominance. Results are presented for the CUAVE database. The two methods achieved 69% and 72% correct ROI extraction. The experiment prompted investigation of a new method using a chrominance based accumulated difference image. The new method achieved 79% correct ROI identification. The overall results suggest that a dual approach using chrominance to locate the mouth region and only employing an accumulated difference image when significant motion is not present would offer good robustness with lower computational cost.

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New color transformation for lips segmentation

Cited by 71 publications

References 6 publications

Parametric models for facial features segmentation

Parametric models for facial features segmentation

4D Automatic Lip-Reading for Speaker's Face Identification

Region of interest extraction using colour based methods on the CUAVE Database

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