Singular Value Decomposition and Principal Component Analysis in Face Images Recognition and FSVDR of Faces

Abstract

The singular value decomposition (SVD) is an important tool for matrix computations with various uses. It is often combined with other methods or used within specific procedures. The text briefly introduces the SVD and lists its important features and selected elements of the SVD theory. In addition, the text deals with two important issues related to the field of artificial intelligence with extensive practical use. The first is face recognition analysis in relation to face representation using principal component analysis (PCA) and the second is fractional order singular value decomposition representation (FSVDR) of faces. The presented procedures can be used in an efficient real-time face recognition system, which can identify a subject’s head and then perform a recognition task by comparing the face to those of known individuals. The essence of the procedures, way of their application, their advantages and shortcomings, and selected results are presented in the text. All procedures are implemented in MATLAB software.