Improved skin detection based on dynamic threshold using multi-colour space

Osman, Mohd Zamri; Maarof, Mohd Aizaini; Rohani, Mohd Foad

doi:10.1109/isbast.2014.7013089

Cited by 5 publications

(4 citation statements)

References 15 publications

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“…The skin color was carried out based on the color of each face skin. We used our skin color detection which introduced in [28][29][30]. The skin color detection was applied as the pre-processing stage to extract the skin color information.…”

Section: Skin Color Feature Extractionmentioning

confidence: 99%

A multi-color based features from facial images for automatic ethnicity identification model

Osman

Maarof

Rohani

et al. 2020

IJEECS

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<span style="font-size: 9pt; font-family: 'Times New Roman', serif;">Ethnicity identification for demographic information has been studied for soft biometric analysis, and it is essential for human identification and verification. Ethnicity identification remains popular and receives attention in a recent year especially in automatic demographic information. Unfortunately, ethnicity identification technique using color-based feature mostly failed to determine the ethnicity classes accurately due to low properties of features in color-based. Thus, this paper purposely analyses the accuracy of the color-based ethnicity identification model from various color spaces. The proposed model involved several phases such as skin color feature extraction, feature selection, and classification. In the feature extraction process, a dynamic skin color detection is adapted to extract the skin color information from the face candidate. The multi-color feature was formed from the descriptive statistical model. Feature selection technique applied to reduce the feature space dimensionality. Finally, the proposed ethnicity identification was tested using several classification algorithms. From the experimental result, we achieved a better result in multi-color feature compared to individual color space model under Random Forest algorithm.</span>

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Section: Skin Color Feature Extractionmentioning

confidence: 99%

A multi-color based features from facial images for automatic ethnicity identification model

Osman

Maarof

Rohani

et al. 2020

IJEECS

Self Cite

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“…The paper in [34] used a dynamic region growing to improve the skin detection rate, Subsequently approaches used first local skin distribution model (LSDM) and its similarity with the global skin distribution model (GSDM) and finally, a fusion based skin model is obtained using both the GSDM and the LSDM. The paper in [98] used multicolor spaces to improve skin detection based on dynamic threshold. The paper in [90] is based on correlation rules between the YCb and YCr subspaces based on dynamic color clustering.…”

Section: B: Region Basedmentioning

confidence: 99%

“…The reason why color space is used in the detection of skin can be improved through the use of combined color spaces as compared to using just one color spaces [56]. The authors in the study carried out in [98] placed emphasis on choosing a color space, in which segmentation algorithm is used, because its great value. In [25], [28], and [98] recent times, YIQ, RGB, YUV, and HSV are widely used.…”

Section: ) Motivation Related To Color Spacementioning

confidence: 99%

“…The authors in the study carried out in [98] placed emphasis on choosing a color space, in which segmentation algorithm is used, because its great value. In [25], [28], and [98] recent times, YIQ, RGB, YUV, and HSV are widely used. The color information for HSV color system is carried by H (Hue) and Saturation (S) components [111].…”

Section: ) Motivation Related To Color Spacementioning

confidence: 99%

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The Landscape of Research on Skin Detectors: Coherent Taxonomy, Open Challenges, Motivations, Recommendations and Statistical Analysis, Future Directions

et al. 2019

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This paper review and analysis the literature on skin detector (SD), in order to establish the coherent taxonomy and figure out the gap on this pivotal research area. An extensive search is conducted to identify articles that deal with skin detection, skin segmentation, skin tone detector, and skin recognition issues, related techniques are reviewed comprehensively and a coherent taxonomy of these articles is established. ScienceDirect, IEEE Xplore, and Web of Science databases are checked for articles on skin detector. A total of 2803 papers are collected from 2007 to February 2018. The set comprised 173 articles. The largest portion of the papers (n = 158/173) = 91% belong to Development and Design, that is aimed to develop an approach for skin classifier into skin and non-skin. A sum total of (n = 5/173) = 3% of the papers belong to Evaluation and Framework, (n = 10/173) = 6% papers was categorized as Comparative Study. This paper discusses the open challenges, motivations and recommendations of the related works. Furthermore, state-of-the-art is a step to demonstrate the novelty of the presented study by conducted a statistical analysis for previous studies such as (Dataset, Color spaces, features, image type, and Classification techniques) as a future direction for other researchers who are interested in SD.

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Accurate Pixel-Wise Skin Segmentation Using Shallow Fully Convolutional Neural Network

et al. 2020

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Skin segmentation plays an important role in human activity recognition, video surveillance, hand gesture identification, face detection, human tracking and robotic surgery. The accurate segmentation of the skin is necessary to recognize the human activity. Segmentation of skin is easy to realize in ideal situations because of similar backgrounds. But it becomes complicated because of presence of skin-like pixels, background illuminations, and certain changes in environment. These problems are addressed by incorporating preprocessing stages in current studies, but this raises the total cost of the system. However, there are some limitations associated with these methods in terms of accuracy and processing speed. In this work, we propose a skin semantic segmentation network (SSS-Net) that is able to capture the multi-scale contextual information and refines the segmentation results especially along object boundaries. Moreover our network helps to reduce the cost of the preprocessing as well. We have performed experiments on the five open datasets of human activity recognition for the segmentation of skin. Experimental results show SSS-Net outperforms the state-of-the-art methods in skin segmentation in terms of accuracies.

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Improved skin detection based on dynamic threshold using multi-colour space

Cited by 5 publications

References 15 publications

A multi-color based features from facial images for automatic ethnicity identification model

A multi-color based features from facial images for automatic ethnicity identification model

The Landscape of Research on Skin Detectors: Coherent Taxonomy, Open Challenges, Motivations, Recommendations and Statistical Analysis, Future Directions

Accurate Pixel-Wise Skin Segmentation Using Shallow Fully Convolutional Neural Network

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