Face Recognition using Self Organizing Map Based on Multi-Level Thresholding and Features Extraction

Abstract

Facial recognition has played a very important role in security systems in recent decades. Various studies have been conducted to find a good method for this recognition to be effective. The facial recognition process in this study uses the self-organizing maps (SOM) method which is based on Multi-Level Thresholding segmentation and extraction of image features. The obtained face image will be segmented using Multi-Level Thresholding and the face will be generated without involving the background. The image that has been segmented will be calculated the values of texture feature extraction using GLCM and color feature extraction using HSV. The resulting image will be carried out by a training and testing process for facial recognition using the self-organizing map (SOM) method. The test results in the proposed study showed good results with an introduction rate of 78.44%. This percentage is higher than face recognition without a segmentation process with a recognition rate of 65.78%. This shows that the proposed method has better performance for the face recognition process without involving the segmentation process.