COMPARISON OF FACIAL IMAGE SEGMENTATION USING K-MEANS AND FUZZY C-MEANS CLUSTERING METHODS - Fitri Nuraeni; Helfy Susilawati; Yoga Handoko Agustin (Jurnal Scientia, 2022)
Accuracy in face recognition is very important, so the process always begins with image segmentation. This segmentation is how the process of dividing the image into several objects, so that the object to be analyzed can be found. The easiest image segmentation is to use the clustering method. However, with so many clustering algorithms, it is necessary to know which algorithm can produce the best image segmentation for facial image datasets taken from employee attendance applications. This study uses an experimental method with image preparation stages, segmentation with k-means and fuzzy c-means algorithms, followed by evaluation using RSME, PSNR, and SSIM. The results of this study indicate that it can be said that for facial image segmentation taken from this employee attendance application, the segmentation of the clustering results with fuzzy c-means has the RMSE, PSNR, and visual effects values ​​needed for segmentation quality. on the image that is better than the image from the k-means segmentation.