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

Charles Victor B. Saragih, Setyawan Widyarto


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.


Full Text:



Bajwa, Usama Ijaz, Imtiaz Ahmad Taj, Muhammad Waqas Anwar, Xuan Wang. (2013). A Multifaceted Independent Performance Analysis of Facial Subspace Recognition Algorithms. Plos One.

Li, Qin, Hua Jing Wang, Jane You, Zhao Ming Li, Jin Xue Li. (2013).

Enlarge the Training Set Based on Inter-ClassRelationship for Face

Recognition from One Image per Person. Plos One.


Diposumarto, Ngadino Surip. (2012). Metodologi Penelitian Teori dan

Terapani. Mitra Wacana Media.

Rakover, Sam S (2002), Featural vs. configurational information in faces: A conceptual and empirical analysis. British Journal of Psychology, ProQuest.

Wagner, Florian (2015). GO-PCA: An Unsupervised Method to Explore

Gene Expression Data Using Prior Knowledge. Plos One.

Galantucci, Luigi, Maria, Eliana Di Gioia, Fulvio Lavecchia, Gianluca Percoco (2014). Is principal component analysis an effective tool to predict face attractiveness? A contribution based on real 3D faces of highly selected attractive women, scanned with stereophotogrammetry. International Federation for Medical and Biological Engineering.

Bart, De Ketelaere, Mia Hubert, Eric Schmitt (2015). Overview of PCA- Based Statistical Process-Monitoring Methods for Time-Dependent, High-Dimensional. Journal of Quality Technology.

Xie, Shengkun, Feng Jin, Sridhar Krishnan, Farook Sattar (2012). Signal feature extraction by multi-scale PCA and its application to respiratory sound classification. International Federation for Medical and Biological Engineering, 2012.


  • There are currently no refbacks.