Lip Segmentation Using Geometrical Model of Color Distribution

Shemshaki, Mehrdad; Amjadifard, Roya

doi:10.1109/iranianmvip.2011.6121606

Cited by 9 publications

(6 citation statements)

References 11 publications

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“…In past decades, many researchers have proposed various lip segmentation approaches [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22] which can basically be divided into three categories depending on the information source they exploit, i.e. color-based, edge-based or spatial information Cheng Guan and Shilin Wang are with the School of Electronic Information and Electrical Engineering, Shanghai Jiaotong University, 200240, Shanghai, China.…”

Section: Introductionmentioning

confidence: 99%

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Lip image segmentation based on a fuzzy convolutional neural network

Guan

Wang

Liew

2019

IEEE Trans. Fuzzy Syst.

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Research has shown that the human lip and its movements are a rich source of information related to speech content and speaker's identity. Lip image segmentation, as a fundamental step in many lipreading and visual speaker authentication systems, is of vital importance. Because of variations in lip color, lighting conditions and especially the complex appearance of an open mouth, accurate lip region segmentation is still a challenging task. To address this problem, this paper proposes a new fuzzy deep neural network having an architecture that integrates fuzzy units and traditional convolutional units. The convolutional units are used to extract discriminative features at different scales to provide comprehensive information for pixel-level lip segmentation. The fuzzy logic modules are employed to handle various kinds of uncertainties and to provide a more robust segmentation result. An end-to-end training scheme is then used to learn the optimal parameters for both the fuzzy and the convolutional units. A dataset containing more than 48,000 images of various speakers, under different lighting conditions, was used to evaluate lip segmentation performance. According to the experimental results, the proposed method achieves state-of-the-art performance when compared with other algorithms.

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Section: Introductionmentioning

confidence: 99%

“…guided approaches. The color-based approaches [8][9] normally detect lip pixels by a preset color filter which is able to differentiate lip and non-lip pixels in a specific color space. The color-based approaches can obtain good segmentation result for lip images where there is a high color contrast.…”

Section: Introductionmentioning

confidence: 99%

Lip image segmentation based on a fuzzy convolutional neural network

Guan

Wang

Liew

2019

IEEE Trans. Fuzzy Syst.

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“…In [31,34,128,129], different elliptical boundaries are estimated based on the fact that skin locus in CbCr is similar to ellipse, and then in evaluation, only pixels surrounded by the ellipse are considered as skin. An FPGA implementation of a face detector based on explicitly defined boundary model in YC b C r color space is proposed in [21].…”

Section: A Explicitly Defined Boundary Modelsmentioning

confidence: 99%

A Comprehensive Survey on Human Skin Detection

Mahmoodi

Sayedi

2016

IJIGSP

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Human Skin detection is one of the most widely used algorithms in vision literature which has been numerously exploited both directly and indirectly in multifarious applications. This scope has received a great deal of attention specifically in face analysis and human detection/tracking/recognition systems. As regards, there are several challenges mainly emanating from nonlinear illumination, camera characteristics, imaging conditions, and intra-personal features. During last twenty years, researchers have been struggling to overcome these challenges resulting in publishing hundreds of papers. The aim of this paper is to survey applications, color spaces, methods and their performances, compensation techniques and benchmarking datasets on human skin detection topic, covering the related researches within more than last two decades. In this paper, different difficulties and challenges involved in the task of finding skin pixels are discussed. Skin segmentation algorithms are mainly based on color information; an in-depth discussion on effectiveness of disparate color spaces is elucidated. In addition, using standard evaluation metrics and datasets make the comparison of methods both possible and reasonable. These databases and metrics are investigated and suggested for future studies. Reviewing most existing techniques not only will ease future studies, but it will also result in developing better methods. These methods are classified and illustrated in detail. Variety of applications in which skin detection has been either fully or partially used is also provided.

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“…Comparatively better results were achieved [14]. Another improvement upon traditional approaches came in 2011 with Shemshaki and Amjadifard's robust algorithm that segmented lip and skin colour [15]. They used chromatic and YCbCr colour spaces to first detect skin and then lip pixel values for segmentation.…”

Section: Literature Reviewmentioning

confidence: 99%

Colour-based lips segmentation method using artificial neural networks

Hassanat

Alkasassbeh

Al-Awadi

et al. 2015

2015 6th International Conference on Information and Communication Systems (ICICS)

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A colour-based technique for lip segmentation is presented throughout this work. Basically, we make use of colour spaces to categorize pixels as either lip or non-lip using artificial neural networks. This study clearly shows how a novel method for fusion of the existing colour spaces practically produces better results than individual colour spaces. More accuracy in dealing with face images under many different conditions has been achieved. Comparing this work with other researchers' work using the same databases, we found that our method, which involves the fusion of different colour information that comes from different colour models, outperforms other methods.

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Lip Segmentation Using Geometrical Model of Color Distribution

Cited by 9 publications

References 11 publications

Lip image segmentation based on a fuzzy convolutional neural network

Lip image segmentation based on a fuzzy convolutional neural network

A Comprehensive Survey on Human Skin Detection

Colour-based lips segmentation method using artificial neural networks

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