Implementasi Pengenalan Wajah Menggunakan PCA (Principal Component Analysis)

Abstract

Identification system grow quickly. The development encourage security system progress based biometric. Face recognition is one of the identification system is developed based on different characteristic of a person's face based biometric which has a high accuracy. Eigenface is one method of face recognition based on PCA (Principal Component Analysis) which easy to implement. It begin with initial processing to get a better image. Then computing eigenvector and eigenvalue from face image for further training image process. Training process is finding the eigenvector, eigenvalue and average image to be projected into the PCA subspace. Projection into the PCA subspace is used to simplify the image data stored. The smallest PCA projection comparison between the database and the input file is determinants the result of username. Smallest value comparison searched using Nearest Neighbor. Face recognition program show one of username that has been stored in a database. The test using smile expression and without expression in eight people and 16 faces. The percentage of successful face recognition process is 82,81%. Several factors that influence the success of the recognition are lighting on the face, the face distance with a webcam, sum face image of people saved and used computer performance.