A COMPARATIVE STUDY OF FACE RECOGNITION BASED ON SELECTED REGIONS WITH PRINCIPAL COMPONENT ANALYSIS (PCA) AND KERNEL PRINCIPAL COMPONENT ANALYSIS (KPCA) AND GABOR FILTERS

Authors

  • Petya Petrova

Keywords:

Comparative Face Recognition, Region of Interest, Recognition Rate

Abstract

The paper presents a comparative study of performance for face recognition algorithms using Principal Component Analysis (PCA) and Kernel Principal Component analysis (KPCA). Images with various Regions of Interest (ROI's) are chosen from the databases to recognise faces. The results of parallel recognition are compared with results of ideal conditions. It has been established that the size of the ROI's affects the rate of recognition

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References

КНК, Т. 6 Бр. 1 (2017)

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Published

2018-05-18

Issue

Vol. 6 No. 1 (2017): Computer Sciences and Communications

Section

Computer Science and Communications - Reviewed Publications. ISSN: 1314-7846

License

Articles published in "Computer Science and Communications" Magazine are licensed under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.  

How to Cite

A COMPARATIVE STUDY OF FACE RECOGNITION BASED ON SELECTED REGIONS WITH PRINCIPAL COMPONENT ANALYSIS (PCA) AND KERNEL PRINCIPAL COMPONENT ANALYSIS (KPCA) AND GABOR FILTERS. (2018). COMPUTER SCIENCES AND COMMUNICATIONS, 6(1), 3-7. https://csc.bfu.bg/index.php/CSC/article/view/34

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