Pengenalan Citra Wajah Menggunakan Metode Analisis Komponen Utama dan Jarak Euclidean

Face recognition is a branch of biometrics, a field of science that uses the physical characteristics of a person to determine or reveal his identity. The method of recognition used is the eigenface method. Eigenface uses the Principal Component Analysis method (PCA) which is a mathematical method of representing an object, extracting the characteristics of an object and reducing an object by means of transforming it using eigenvalue and eigenvector linearly. The result of this eigenface process is the extraction of the main features of the face image. The facial image used for the study are divided into the image of training and the test image. The extraction of this main feature of these two images will then be measured at the recognition accuracy using the euclidean distance method. The smaller the distance between the 2 face images vectors, the greater the degree of similarity between the two face images. Based on the research results, the accuracy for the test image of experiencing a change of 100%. For to test images that undergo changes such as noise addition, addition of accessories and test image by raising and decreasing the brightness levels obtained a good accuracy level of 85.7% while test images that experience cropping and rotation can not be recognized with an accuracy of 0%