Facial recognition system using eigenfaces and PCA

Document Type : Original Article

Authors

1 Iran University of Science and Technology, School of Mathematics, Tehran, Iran.

2 Department of Mathematics and Statistics, Imam Hossein Comprehensive University, Tehran, Iran.

Abstract

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).

Keywords

Main Subjects


[1] H. Abdi, L.J. Williams, Principal component analysis, WIREs Comp Stat, 2(4) 2010, 433-459.
[2] M. Agarwal, H. Agrawal, N. Jain, M. Kumar, Face Recognition Using Principle Component Analysis, Eigenface and Neural Network, International Conference on Signal Acquisition and Processing, 2 2010, 310-314.
[3] C.S. Chang, T.L. Liao, P.Y. Hsu, K.K. Chen, Human face recognition system using modified PCA algorithm and ARM platform, WIREs Comp Stat vol.2 (2010), Proceedings of Computer Communication Control and Automation (3CA), 294-297.
[4] C.D. Meyer, Matrix analysis and applied linear algebra, Society for Industrial and Applied Mathematics (SIAM), Philadelphia, 2000.
[5] E.M Azriansyah, N. Hartuti, M. Fachrurrozi, B.A. Tama, A Study about Principle Component Analysis and Eigenface for Facial Extraction, J. Phys. Conf. Ser. 2 2019, 240-258.
[6] I. Gohberg, P. Lancaster, L. Rodman, Face Recognition Using Principle Component Analysis, Eigen face and Neural Network, Indefinite linear algebra and applications, Birkhuser Verlag, 2005, 310-314.
[7] P. Kamencay, R. Hudec, M. Benco, M. Zachariasova, 2D-3D Face Recognition Method Based on a Modified CCA-PCA Algorithm, International Journal of Advanced Robotics Systems, 2014, 215-230.
[8] H. Moon, P.J. Philips, Computational and performance aspects of PCA-based face recognition algorithms, Perception, 30 2001, 303-321.
[9] L. Sirovich, M. Kirby, Low-Dimensional Procedure for the Characterization of Human Faces, Journal of the Optical Society of America A, 4(3) 1987, 519-524.
[10] M.A. Turk, A.P. Pentland, Face recognition using eigenfaces, Proceedings of 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1991, 586-591.