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DOI: 10.14569/IJACSA.2019.0100647
PDF

Dense Hand-CNN: A Novel CNN Architecture based on Later Fusion of Neural and Wavelet Features for Identity Recognition

Author 1: Elaraby A. Elgallad
Author 2: Wael Ouarda
Author 3: Adel M. Alimi

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 10 Issue 6, 2019.

  • Abstract and Keywords
  • How to Cite this Article
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Abstract: Biometric recognition or biometrics has emerged as the best solution for criminal identification and access control applications where resources or information need to be protected from unauthorized access. Biometric traits such as fingerprint, face, palmprint, iris, and hand-geometry have been well explored; and matured approaches are available in order to perform personal identification. The work emphasizes the opportunities for obtaining texture information from a palmprint on the basis of such descriptors as Curvelet, Wavelet, Wave Atom, SIFT, Gabor, LBP, and AlexNet. The key contribution is the application of mode voting method for accurate identification of a person at the fusion decision level. The proposed approach was tested in a number of experiments at the CASIA and IITD palmprint databases. The testing yielded positive results supporting the utilization of the described voting technique for human recognition purposes.

Keywords: Deep learning; fusion; palmprint; squeezenet; voting

Elaraby A. Elgallad, Wael Ouarda and Adel M. Alimi, “Dense Hand-CNN: A Novel CNN Architecture based on Later Fusion of Neural and Wavelet Features for Identity Recognition” International Journal of Advanced Computer Science and Applications(IJACSA), 10(6), 2019. http://dx.doi.org/10.14569/IJACSA.2019.0100647

@article{Elgallad2019,
title = {Dense Hand-CNN: A Novel CNN Architecture based on Later Fusion of Neural and Wavelet Features for Identity Recognition},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2019.0100647},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0100647},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {6},
author = {Elaraby A. Elgallad and Wael Ouarda and Adel M. Alimi}
}



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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