Acne Classification with Gaussian Mixture Model based on Texture Features

This paper presents an acne detection method on face images using a Gaussian Mixture Model (GMM). First, the skin area in the face image is segmented based on color information using the GMM. Second, the candidates of the acne region are then extracted using a Laplacian of Gaussian-based blob detection strategy. Then, texture features are extracted from acne candidates using either a Gabor Filter or Gray Level Co-occurrence Matrix (GLCM). Lastly, these features are then utilized as input in the GMM for verifying whether these regions are acne or not. In our experiment, the proposed method was evaluated using face images from ACNE04 dataset. Based on the experiment, it is found that the best classification results were obtained when GLCM features in the Cr-YCbCr channel are applied. In addition, the proposed method has competitive performance compared to K-Nearest Neighbor (KNN).

@article{Maimanah2022,
  title     = {Acne Classification with Gaussian Mixture Model based on Texture Features},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {8},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Alfa Nadhya Maimanah and Wahyono and Faizal Makhrus},
  doi       = {10.14569/IJACSA.2022.0130844},
  url       = {https://doi.org/10.14569/IJACSA.2022.0130844}
}