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Neural Computing and Applications

, Volume 31, Issue 6, pp 1835–1853 | Cite as

An optimized skin texture model using gray-level co-occurrence matrix

  • Mahdi Maktabdar OghazEmail author
  • Mohd Aizaini Maarof
  • Mohd Foad Rohani
  • Anazida Zainal
  • Syed Zainudeen Mohd Shaid
Original Article

Abstract

Texture analysis is devised to address the weakness of color-based image segmentation models by considering the statistical and spatial relations among the group of neighbor pixels in the image instead of relying on color information of individual pixels solely. Due to decent performance of the gray-level co-occurrence matrix (GLCM) in texture analysis of natural objects, this study employs this technique to analyze the human skin texture characteristics. The main goal of this study is to investigate the impact of major GLCM parameters including quantization level, displacement magnitudes, displacement direction and GLCM features on skin segmentation and classification performance. Each of these parameters has been assessed and optimized using an exhaustive supervised search from a fairly large initial feature space. Three supervised classifiers including Random Forest, Support Vector Machine and Multilayer Perceptron have been employed to evaluate the performance of the feature space subsets. Evaluation results using Edith Cowan University (ECU) dataset showed that the proposed texture-assisted skin detection model outperformed pixelwise skin detection by significant margin. The proposed method generates an F-score of 91.98, which is satisfactory, considering the challenging scenario in ECU dataset. Comparison of the proposed texture-assisted skin detection model with some state-of-the-art skin detection models indicates high accuracy and F-score of the proposed model. The findings of this study can be used in various disciplines, such as face recognition, skin disorder and lesion recognition, and nudity detection.

Keywords

Gray-level co-occurrence matrix Skin segmentation Texture analysis GLCM Skin texture 

Notes

Acknowledgements

The authors would like to acknowledge ministry of higher education (MOHE) and Universiti Teknologi Malaysia (UTM) for supporting this research under Research University Grant (RUG) vote 10J28 and science fund Grant (MOSTI) vote 01-01-06-SF1167.

Compliance with ethical standards

Conflict of interest

We would like to state that there is no conflict of interest to declare and corresponding author shall bear full responsibility for the submission.

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Copyright information

© The Natural Computing Applications Forum 2017

Authors and Affiliations

  • Mahdi Maktabdar Oghaz
    • 1
    Email author
  • Mohd Aizaini Maarof
    • 1
  • Mohd Foad Rohani
    • 1
  • Anazida Zainal
    • 1
  • Syed Zainudeen Mohd Shaid
    • 1
  1. 1.IASRG Lab, 4th Floor, Faculty of ComputingUniversiti Teknologi Malaysia (UTM)Skudai, Johor BahruMalaysia

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