Implementation of NOGIE and NOWGIE for Human Skin Detection

The Digital image processing is one of the most widely implemented fields worldwide. The most applied applications of digital image processing are facial recognition, finger print recognition, medical imaging, law enforcement, cyber-crime investigation, identification of various diseases and criminals, etc. The subject to be discussed in this article is skin detection. Skin detection has solved many serious problems related to digital image process. It is one of the main features in making an intelligent image processing system. The proposed methodology conducts an improved and well enhanced skin detection, the skin and non-skin parts are divided from an input image or video, noise is removed, HSV is applied which also acts as a color model that generates more better results in accordance to RGB or YCbCr for skin and face identification. The algorithms, NOGIE (Noise Object Global Image Enhancement) and NOWGIE (Noise Object with Global Image Enhancement) are applied separately on the input and the results can be compared for better perception and understanding of the applied skin detection techniques, the skin parts are highlighted as “White” while the Non-skin parts are highlighted as “Black”. The results are different NOWGIE gives better results than the NOGIE due to the image enhancement technique. This methodology is subjected to be implemented in special security drones for the identification of suspects, terrorists and spy’s the algorithms provides the ability to detect humans from a non-skin background making an autonomous and excellent security system.

@article{Aftab2018,
  title     = {Implementation of NOGIE and NOWGIE for Human Skin Detection},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {9},
  number    = {7},
  year      = {2018},
  publisher = {The Science and Information Organization},
  author    = {M. Omer Aftab and Junaid Javed and M. Bilal and Arfa Hassan and M. Adnan Khan},
  doi       = {10.14569/IJACSA.2018.090719},
  url       = {https://doi.org/10.14569/IJACSA.2018.090719}
}