Face Recognition Model Development with Euclidean Distance On Eigen Space with 2DPCA

Abstract

Face recognition is an issue that is based on object recognition. Objects identified with specific and measurable characteristics. In the process of face recognition methods quite a lot that can be used, one using the Euclidean distance function method. Euclid's method is the method by comparing the image of testing with the minimum distance to the image database training. Feature extraction techniques used in face recognition to find important features of the training image database that will be a reference. One is a technique introduced by Yang and Zhang in 2004 was 2DPCA (2 Dimensional Principal Component Analysis). Our experiments with used 2DPCA for feature extraction techniques at face recognition. The results achieved that the performance of the model with a percentage correct of 98.75%