A new approach to principal component analysis (PCA) is proposed for functional data. In prevailing methods of functional principal component analysis (FPCA), the definition of a mean is in the form of a function. However, data centralisation based on this kind of mean actually obtains a residual function. The result of FPCA, given its matrix of residual functions, may thus fail to present the essential variation of the original data. Besides, applications in FPCA are mainly for types of one sample problems. Numerical characteristics of functional data are defined as real constants. Centralisation in terms of constant numerical characteristics implies the relocation of the entire matrix of functional variances in order to obtain original curves whose centres of gravity are settled on the origin. Furthermore, based on the covariance matrix obtained from constant numerical characteristics, functional principal components for multivariate sample problems are proposed. Conclusions are validated by simulation in a real situation.