Comparative Study of Statistical Skin Detection Algorithms for Sub-Continental Human Images

Tabassum, Mirza Rehenuma; Gias, Alim Ul; Kamal, Md. Mostafa; Muctadir, Hossain Muhammad; Ibrahim, Mohd Nordin; Shakir, Asif Khan; Imran, Ashiq; Islam, Shafiqul; Rabbani, Md. Golam; Khaled, Shah Mostafa; Islam, Mofakharul; Begum, Zareena

doi:10.3923/itj.2010.811.817

Cited by 15 publications

(14 citation statements)

References 9 publications

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“…A general human skin is characterized by a combination of red and melanin (yellow, brown) and there is somewhat a range of hue for skin and saturation that represent skin-like pixels. YUV is the color space used in the PAL system of television broadcasting which is the standard in most of Europe and some other places [9].…”

Section: B Skin Color Segmentationmentioning

confidence: 99%

A Neural Network Approach for Real Time Emotion Recognition

Chavhan¹,

Chavan²,

Dahe³

et al. 2015

International Journal of Advanced Research in Computer and Comm

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Effective Human Computer Intelligent Interaction (HCII) requires the information about the user's identity, state and intent which can be extracted from images, so that computers can then react accordingly, e.g. systems behaving according to the emotional state of the person. The most expressive way humans display emotions is through facial expressions. In this paper we have presented the technique of recognizing emotion using facial expressions which is of prime focus in human interactions. By creating machines that can detect and understand emotion, we can enhance the human computer interaction. In this paper, we have discussed a framework for the classification of emotional states, based on images of the face and the implementation details of a real-time facial feature extraction and emotion recognition application. This application automatically detects faces from the captured images and codes them with respect to 7 dimensions in real time: Neutral, Anger, Disgust, Fear, Smile, Sadness, and Surprise. Most interestingly the outputs of the classifier change smoothly as a function of time, providing a possibly worth representation of code facial expression dynamics in a fully automatic and unnoticeable manner. The main objective of our work is the real-time implementation of a facial emotion recognition system.

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Section: B Skin Color Segmentationmentioning

confidence: 99%

A Neural Network Approach for Real Time Emotion Recognition

Chavhan¹,

Chavan²,

Dahe³

et al. 2015

International Journal of Advanced Research in Computer and Comm

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“…It is found in present work that separating illumination from chrominance can produce good result in detecting skin pixel [2]. Generally human skin is characterized by a combination of red and melanin (yellow, brown) and there is somewhat a range of hue for skin and saturation that represent skin pixels [10]. The detection of human skin effected by various external factors, such as types of cameras used to capture the image, light-setting, human race and color spaces [1].…”

Section: Introductionmentioning

confidence: 99%

An Analysis of Skin Pixel Detection using Different Skin Color Extraction Techniques

Surampalli¹,

Dayanand²,

Dhananjay³

2012

IJCA

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Automated skin detection from a captured natural image has wide range of application. Detection of skin area in a given image is done through marking skin and non skin pixels. Process of identification of skin pixel is closely associated with color space being used. To select suitable method to extract skin region has motivated this paper. We are using multiple color spaces in a paper to analyze and compare them. We have the different set of images to compare color space. The results indicate that YCbCr provide better performance compare to other color space. General Terms:Color space, Skin pixel

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“…Listed below are the static filters that are described by [21] for skin regions extraction: 70 < < 175 20 < < 102 −48 < < 150 (19) Where:…”

Section: Yiq and Yuv Color Modelsmentioning

confidence: 99%

“…The luminance channel (Y) is separated from the chromatic channels (U, V) to reduce the effect of lighting variations. The conversion of the RGB color model to YUV color model is achieved using the equations given below [21]: …”

Section: Yuv Color Modelmentioning

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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Comparative Study of Statistical Skin Detection Algorithms for Sub-Continental Human Images

Cited by 15 publications

References 9 publications

A Neural Network Approach for Real Time Emotion Recognition

A Neural Network Approach for Real Time Emotion Recognition

An Analysis of Skin Pixel Detection using Different Skin Color Extraction Techniques

Skin Segmentation Using YUV and RGB Color Spaces

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