facial recognition system using eigenfaces and pca

Face recognition is an essential field of image processing and computer vision. in this paper, we have developed a facial recognition system that can detect and recognize the face of a person by comparing the characteristics, and features of the face to those of known faces. our approach considers the face recognition problem as an intrinsically two-dimensional recognition problem rather than requiring recovery of three-dimensional geometry, considering that eigenvectors generally describe human faces in the face space. the system works by projecting face images onto a feature space that spans the significant variations among known face images that are called eigenvectors (or principal components of the face set). our technique can learn and recognize new faces in an unsupervised style—this approach is based on eigenfaces and principal component analysis (pca).