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DOI: 10.14569/IJACSA.2020.0110556
PDF

Multiclass Pattern Recognition of Facial Images using Correlation Filters

Author 1: Nisha Chandran S
Author 2: Charu Negi
Author 3: Poonam Verma

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 5, 2020.

  • Abstract and Keywords
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Abstract: Pattern Recognition comes naturally to humans and there are many pattern recognition tasks which humans can perform admirably well. However, human pattern recognition cannot compete with machine speed when the number of classes to be recognized becomes tremendously large. In this paper, we analyze the effectiveness of correlation filters for pattern classification problems. We have used Distance Classifier Correlation Filter (DCCF) for pattern classification of facial images. Two essential qualities of a correlation filter are distortion tolerance and discrimination ability. DCCF transposes the feature space in such a way that the images belonging to the same class gets closer and the images from different class moves far apart; thereby increasing the distortion tolerance and the discrimination ability. The results obtained demonstrate the effectiveness of the approach for face recognition applications.

Keywords: Pattern recognition; correlation filter; multiclass recognition

Nisha Chandran S, Charu Negi and Poonam Verma, “Multiclass Pattern Recognition of Facial Images using Correlation Filters” International Journal of Advanced Computer Science and Applications(IJACSA), 11(5), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110556

@article{S2020,
title = {Multiclass Pattern Recognition of Facial Images using Correlation Filters},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110556},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110556},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {5},
author = {Nisha Chandran S and Charu Negi and Poonam Verma}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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