An efficient method for face recognition using Principal Component Analysis (PCA). The PCA has been extensively employed for face recognition algorithms. It is one of the most popular representation methods for a face image. It not only reduces the dimensionality of the image, but also retains some of the variations in the image data. The system functions by projecting face image onto a feature space that spans the significant variations among known face images. The significant features are known as \"Eigen faces\", because they are the eigenvectors (principal component analysis) of the set of faces they do not necessarily correspond to the features such as eyes, ears, and noses.