Face detection receives immense interest in computer vision to improve security and authenticity of a particular system. Color provides useful information at the early stage of face detection in a complex view. Such detection involves many complexities such as background, illumination, and poses. This study provides depth analysis on most prominent color models. The use of those color models can handle well-defined problems in face detection such as occlusions, poses, and illumination conditions. The application areas, techniques used, remarks as well as statistical conversion of the color models from Red Green Blue (RGB) color model are demonstrated. Moreover, a new framework for efficient face detection using skin color segmentation is proposed. The process involves transforming the face images from RGB to the selected color models; then segmentation is carried out by selecting a threshold value for each of the color models. Watershed algorithm is applied to isolate the facial feature from the background. Finally, lips area is localized as it may be missing during the detection process. Detection rate of up to 97.22% was obtained using standard database. The proposed framework targets a range of applications such as PC login security, passport authentication, and pornography filtering.
| Published in | American Journal of Artificial Intelligence (Volume 1, Issue 1) |
| DOI | 10.11648/j.ajai.20170101.14 |
| Page(s) | 29-35 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2017. Published by Science Publishing Group |
Face Detection, Color Model, Watershed Algorithm, RGB
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APA Style
Abdulganiyu Abdu Yusuf, Fatma Susilawati Mohamad, Zahraddeen Sufyanu. (2017). Human Face Detection Using Skin Color Segmentation and Watershed Algorithm. American Journal of Artificial Intelligence, 1(1), 29-35. https://doi.org/10.11648/j.ajai.20170101.14
ACS Style
Abdulganiyu Abdu Yusuf; Fatma Susilawati Mohamad; Zahraddeen Sufyanu. Human Face Detection Using Skin Color Segmentation and Watershed Algorithm. Am. J. Artif. Intell. 2017, 1(1), 29-35. doi: 10.11648/j.ajai.20170101.14
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Download@article{10.11648/j.ajai.20170101.14,
author = {Abdulganiyu Abdu Yusuf and Fatma Susilawati Mohamad and Zahraddeen Sufyanu},
title = {Human Face Detection Using Skin Color Segmentation and Watershed Algorithm},
journal = {American Journal of Artificial Intelligence},
volume = {1},
number = {1},
pages = {29-35},
doi = {10.11648/j.ajai.20170101.14},
url = {https://doi.org/10.11648/j.ajai.20170101.14},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajai.20170101.14},
abstract = {Face detection receives immense interest in computer vision to improve security and authenticity of a particular system. Color provides useful information at the early stage of face detection in a complex view. Such detection involves many complexities such as background, illumination, and poses. This study provides depth analysis on most prominent color models. The use of those color models can handle well-defined problems in face detection such as occlusions, poses, and illumination conditions. The application areas, techniques used, remarks as well as statistical conversion of the color models from Red Green Blue (RGB) color model are demonstrated. Moreover, a new framework for efficient face detection using skin color segmentation is proposed. The process involves transforming the face images from RGB to the selected color models; then segmentation is carried out by selecting a threshold value for each of the color models. Watershed algorithm is applied to isolate the facial feature from the background. Finally, lips area is localized as it may be missing during the detection process. Detection rate of up to 97.22% was obtained using standard database. The proposed framework targets a range of applications such as PC login security, passport authentication, and pornography filtering.},
year = {2017}
}
TY - JOUR T1 - Human Face Detection Using Skin Color Segmentation and Watershed Algorithm AU - Abdulganiyu Abdu Yusuf AU - Fatma Susilawati Mohamad AU - Zahraddeen Sufyanu Y1 - 2017/07/24 PY - 2017 N1 - https://doi.org/10.11648/j.ajai.20170101.14 DO - 10.11648/j.ajai.20170101.14 T2 - American Journal of Artificial Intelligence JF - American Journal of Artificial Intelligence JO - American Journal of Artificial Intelligence SP - 29 EP - 35 PB - Science Publishing Group SN - 2639-9733 UR - https://doi.org/10.11648/j.ajai.20170101.14 AB - Face detection receives immense interest in computer vision to improve security and authenticity of a particular system. Color provides useful information at the early stage of face detection in a complex view. Such detection involves many complexities such as background, illumination, and poses. This study provides depth analysis on most prominent color models. The use of those color models can handle well-defined problems in face detection such as occlusions, poses, and illumination conditions. The application areas, techniques used, remarks as well as statistical conversion of the color models from Red Green Blue (RGB) color model are demonstrated. Moreover, a new framework for efficient face detection using skin color segmentation is proposed. The process involves transforming the face images from RGB to the selected color models; then segmentation is carried out by selecting a threshold value for each of the color models. Watershed algorithm is applied to isolate the facial feature from the background. Finally, lips area is localized as it may be missing during the detection process. Detection rate of up to 97.22% was obtained using standard database. The proposed framework targets a range of applications such as PC login security, passport authentication, and pornography filtering. VL - 1 IS - 1 ER -
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