Information System Of Administration Village Demography To Accelerate Villager Condition And Mutation Report

Abstract

Face recognition is high enough to get attention, because face recognition can be applied to many potential applications, such as identity authentication, information security, surveillance and human computer interaction. This study aims to build a Matlab-based software for detection and face recognition with image input form. System to be built include the detection and face recognition. Face Detection subsystem using PCA as feature extraction and Back Propagation Neural Network as classifier.

Face Recognition Subsystem using Support Vector Machine method as one of the artificial intelligence algorithm that is able to classify many faces well. Multiple Discriminant Analysis Method / Fukunaga Koontz Transforms (MDA / FKT) is used as feature extraction. Training and Testing Database systems using UNUD Database, and ORL Database as a comparison.

Design of Application Detection and Face Recognition has been successfully completed in this study, the test program using MATLAB. Face Detection subsystem produces face detection accuracy rate of 99%. At Face Recognition Subsystem, the recognition rate 82.76% on UNUD Database with projections of 53 dimensions. While the recognition rate on ORL Database 97.5%.