Face Recognition with Principal Component Analysis (PCA) Application Using Euclidean Distance Measurement

Charles Victor B. Saragih, Setyawan Widyarto

Abstract


Face recognition algorithms can be categorized into

5 based methods of linear and non-linear projection, namely: artificial neural network-based method of non-linear, Gabor filters and wavelets based methods, fractal-based methods and methods based on thermal and hyperspectral. PCA is a statistical method that can explain the formulation of artificial neural networks and is designed to process the multidimensional information. With the PCA method to do efficient calculation where multidimensional information can be simplified into a number of variables, dimensions and factors serve as the basic component. Many researchers use PCA method that allows modeling of a human face by using the parameters in limited quantities. One advantage PCA method is the ability to process high-dimensional data modeling that cannot be done by many other methods because it requires a covariant matrix inverse. PCA is a better method than matching pursuit (MP), especially on the use of time, fast and efficient. The purpose of this study was to analyze the images using image recognition algorithms to calculate the distance euclidean PCA. The result of this research is the image recognition can be performed using PCA algorithm to form a basis vector as the basis for calculating the normalization of an image. Euclidean distance calculations will provide clarity regarding the degree of similarity and dissimilarity drawing a picture.

 


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