Analysis of Face Recognition with Fuzzy C-Means Clustering Image Segmentation and Learning Vector Quantization

Abstract

Digital image processing is used to analyze images consisting of visual perceptions whose purpose is to manipulate, modify the image, improve the quality of the image and prioritize the special features contained in the image by using the special attributes of the image characteristics that can be grouped based on the similarity of the image characteristics with each other. One method used in image recognition from artificial neural networks is Learning Vector Quantization (LVQ). The application of the LVQ method for face recognition requires a feature segmentation process so that the feature extraction values obtained only cover the face, this stage is carried out using the Fuzzy C-Means and K-Means Clustering methods. The results of face recognition testing using the LVQ method and K-Means Clustering segmentation obtained an accuracy of 92.22% (training) and 72.5% (testing). Face recognition with Fuzzy C-Means Clustering segmentation obtained face recognition results of 95.78% (training) and 76% (testing). The LVQ-FCM method is better than the LVQ with KMeans, obtained training accuracy of 3.56% and 3.5% for network testing.