Facial Skin Segmentation Based on Color Images using Combined Approach of Self Organizing Map and Neural Gas Network Applicable to Facial Plastic Surgeries

Document Type : Original Article


1 Faculty of Biomedical Engineering, Sahand University of Technology, Tabriz, Iran

2 Faculty of Biomedical Engineering,, Sahand University of Technology, Tabriz, Iran


 Facial image segmentation is an essential component in applications of image processing and computer vision such as face recognition, identity recognition and analysis of facial plastic surgery. The clustering based methods are one of the important methods in the facial image segmentation. Self-Organizing Map (SOM) is a powerful method in the data mining. A main disadvantage of the SOM algorithm is that learning coefficient is not adaptive in this algorithm. Adaptability of learning coefficient in the adaptation phase can improve the performance of the SOM clustering. Neural Gas Network (NGN) is an unsupervised learning that its neighborhood structure is adaptive and synaptic weight is updated without any topological adjustment. The main purpose of this study is to present a new hybrid SOMNGN method in which the learning coefficient is adapted in the adaptation phase of the SOM algorithm using the NGN algorithm. Also, two color spaces, including YCbCr and Face Mapping are used for facial skin modelling as a pre-processing step. Obtained results in the mentioned color spaces show that presented method have the higher accuracy than the standard SOM method.


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