Face Recognition Based on Principal Component Analysis

Full Text (PDF, 1386KB), PP.38-44

Views: 0 Downloads: 0

Author(s)

Engr Ali Javed 1,*

1. Faculty of Telecom & Information Engineering, University of Engineering & Technology, Taxila

* Corresponding author.

DOI: https://doi.org/10.5815/ijigsp.2013.02.06

Received: 2 Nov. 2012 / Revised: 6 Dec. 2012 / Accepted: 8 Jan. 2013 / Published: 8 Feb. 2013

Index Terms

PCA, Eigen Faces, Data matrix, Face Detection, Face Recognition, Gaussian Filter

Abstract

The purpose of the proposed research work is to develop a computer system that can recognize a person by comparing the characteristics of face to those of known individuals. The main focus is on frontal two dimensional images that are taken in a controlled environment i.e. the illumination and the background will be constant. All the other methods of person's identification and verification like iris scan or finger print scan require high quality and costly equipment's but in face recognition we only require a normal camera giving us a 2-D frontal image of the person that will be used for the process of the person's recognition. Principal Component Analysis technique has been used in the proposed system of face recognition. The purpose is to compare the results of the technique under the different conditions and to find the most efficient approach for developing a facial recognition system

Cite This Paper

Ali Javed,"Face Recognition Based on Principal Component Analysis", IJIGSP, vol.5, no.2, pp.38-44, 2013. DOI: 10.5815/ijigsp.2013.02.06

Reference

[1]Pawan Sinha, Benjamin Balas, Yuri Ostrovsky, and Ric hard Russell, "Face Recognition by Humans: Nineteen Results All Computer Vision Researchers Should Know About" Proceedings of the IEEE, Vol. 94, No. 11, November 2006 

[2]G. Shakhnarovich, J. Fisher, and T. Darrell. Face recognition from long-term observations. In ECCV, 2002. 

[3]K. Chang, K. Bowyer, and P. Flynn, "Multi-modal 2d and 3d biometrics for face recognition," to appear in IEEE International Workshop on Analysis and Modeling of Faces and Gestures, 2003.

[4]B. Moghaddam and A. Pentland. Probabilistic visual learning for object representation. IEEE Trans. Pattern Analysis and Machine Intell, 19:696–710, 1997.

[5]A. N. Rajagopalan, K. S. Kumar, J. Karlekar, R. Manivasakan, M. M. Patil, U. B. Desai, P. G. Poonacha, and S. Chaudhuri. Locating human faces in a cluttered scene. Graphical Models in Image Processing, 62:323–342, 2000.

[6]D. A. Socolinsky and A. Selinger, "A comparative analysis of face recognition performance with visible and thermal infrared imagery," in International Conference on Pattern Recognition, pp. IV: 217–222, August 2002.

[7]A.J. GoldStein, L.D Harmon, and A.B Lesk, "Identification of human faces", Proc IEEE, May 1971, Vol. 59, No. 5, 748-760.

[8]L. Sirovich and M Kirby, "A low dimensional Procedure for the characterization of human faces," J Optical. Soc. Am. A, 1987, Vol. 4, No. 3, 519-524.

[9]M. A .Turk and A. P. Pentland, "Face Recognition using Eigen Faces," Proc. IEEE, 1991, 586-591.

[10]Kwang In Kim, Keechul Jung, and Hang Joon Kim, "Face recognition using kernel principal component analysis," Signal Processing letters IEEE, vol. 9 Issue. 2 page 40-42 Feb, 2002.

[11]Xin Chen, Patrick J. Flynn, Kevin W. Bowyer, "PCA-Based Face Recognition in Infrared Imagery: Baseline and Comparative Studies," amfg, pp.127, IEEE International Workshop on Analysis and Modeling of Faces and Gestures, 2003

[12]Neerja, Ekta Walia, "Face Recognition Using Improved Fast PCA Algorithm", International Congress on Image and Signal Processing CISP 2008, in Sanya, Hainan, China, Vol. 1, pp. 554-558, 27-30 May 2008.