Human face recognition with combination of DWT and machine learning. Tabassum, F., Imdadul Islam, M., Tasin Khan, R., & Amin, M. R. Journal of King Saud University - Computer and Information Sciences, February, 2020.
Human face recognition with combination of DWT and machine learning [link]Paper  doi  abstract   bibtex   
To enhance the accuracy of object recognition, various combination of recognition algorithms are used in recent literature. In this paper coherence of Discrete Wavelet Transform (DWT) is combined with four different algorithms: error vector of principal component analysis (PCA), eigen vector of PCA, eigen vector of Linear Discriminant Analysis (LDA) and Convolutional Neural Network (CNN) then combination of four results are done using entropy of detection probability and Fuzzy system. From this research the accuracy of recognition is found dependent on image and diversity of database. The combined method of the paper provides recognition rate of 89.56% for the worst case and 93.34% for the best case both can be said better in comparison with the previous works where individual method has been implemented on a specific set of images.
@article{tabassum_human_2020,
	title = {Human face recognition with combination of {DWT} and machine learning},
	issn = {1319-1578},
	url = {https://www.sciencedirect.com/science/article/pii/S1319157819309395},
	doi = {10.1016/j.jksuci.2020.02.002},
	abstract = {To enhance the accuracy of object recognition, various combination of recognition algorithms are used in recent literature. In this paper coherence of Discrete Wavelet Transform (DWT) is combined with four different algorithms: error vector of principal component analysis (PCA), eigen vector of PCA, eigen vector of Linear Discriminant Analysis (LDA) and Convolutional Neural Network (CNN) then combination of four results are done using entropy of detection probability and Fuzzy system. From this research the accuracy of recognition is found dependent on image and diversity of database. The combined method of the paper provides recognition rate of 89.56\% for the worst case and 93.34\% for the best case both can be said better in comparison with the previous works where individual method has been implemented on a specific set of images.},
	language = {en},
	urldate = {2021-05-17},
	journal = {Journal of King Saud University - Computer and Information Sciences},
	author = {Tabassum, Fahima and Imdadul Islam, Md. and Tasin Khan, Risala and Amin, M. R.},
	month = feb,
	year = {2020},
	keywords = {Accuracy of recognition, Eigen vectors, FC, ROI, Wavelet coherence},
}

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