Application of principal component analysis for face recognition based on weighting matrix using Gui Matlab

Y.A. Lesnussa, N.A Melsasail, Z.A Leleury,

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Type :	article
Subject :	TA Engineering (General). Civil engineering (General)
ISSN :	2289-7070
Main Author :	Y.A. Lesnussa, N.A Melsasail, Z.A Leleury,
Title :	Application of principal component analysis for face recognition based on weighting matrix using Gui Matlab

Place of Production :	Universiti Pendidikan Sultan Idris
Year of Publication :	2016

Abstract :

The increasingly widespread of using computers in daily life has brought the piranti as assistant for human. One of the application computer in the field of security has increased it is role is in terms of facial recognition. Face recognition is the process of human identification with the face image. With the increasingly widespread use of computers, is expected to face recognition capabilities can be adapted on the smart device. The adaption process became possible with the discovery of variety of facial recognition methods, one of which is the Principal Component Analysis (PCA). Research began by designing a computer program using the programming language Matlab. The program is used to test the results of PCA with several facial image. At the end it can be concluded that PCA is quite worthy of a face recognition method. The research data shows recognition results were pretty good with quite small error rate.

References

1. Atalay, I. (1996). Face recognition using eigenfaces. Istambul Technical University, Turki. 2. A.M. Martinez and A.C. Kak. (2001). PCA versus LDA. IEEE Trans. Pattern Anal. Mach. Intell., Vol. 23, no. 2, pp. 228-233. 3. Arif Muntasa. (2013). Line detection model and adaptive threshold based image segmentation for handwriting word regocnition. International Conference on Electrical, Informatics and Its Education, Malang State University. 4. Arif Muntasa, Hariadi. M, Purnomo. H. M. (2009). A new formulation of face sketch multiple features detection using pyramid parameter model and simultaneous landmark movement. International Journal of Computer Science and Network Security, Vol. 9 Nomor 9. 5. Arif Muntasa, Hariadi. M, Purnomo. H. M. (2008). Penyeleksian eigenface secara otomatis untuk pengenalan citra wajah. SITIA, ITS. 6. Calder, A.J., Burton, A.M, Miller, P., Young, A.W., and Akamatsu, S. (2001). A principle componen analysis of facial expressions. Vision Research 41: 1179-1208. 7. Jianke, Z., Mang, V. and Peng, U.M. (2002). Face Recognition Using 2D DCT with PCA. University of Macau, Macau. 8. Kresimir Delac, Mislav Grgic, Panos Liatsis. (2005). Appearance Based Statistical Methods for Face Regocnition. 47th International Symposium ELMAR, Zadar, Croatia Sidik jari. 9. Sax, J.D., and Dillon, O.W. (1963). The simulation of radiation. Radiation Journal, No. 2, Addison-Wisley, San Fransisco, 173-179. 10. Turk, M., and Pentland. (1991). Eigenfaces for Recognition. Journal of Cognitive Neuroscience 3: 71-86.

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