International Journal of Data Science and Analysis

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Multimodal Biometrics Data Analysis for Gender Estimation Using Deep Learning

Received: Dec. 09, 2019    Accepted: Dec. 16, 2019    Published: May 29, 2020
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Abstract

In the recent past with the rapid growing technology security problem is ubiquitous to our daily life pertinent to it, now a day the usage of biometrics is becoming inevitable. Correspondingly, the field of biometrics has gained tremendous acceptance because of its individualistic and authentication capabilities. In many practical scenario the multimodal-based gender estimation will helps to increase the security and efficiency of other biometrics system. Likewise, in contrast to it uni-modal biometric, the multimodal biometrics system would be very difficult to spoof because of its multiple distinct biometrics features. Gender identification using biometrics traits are mainly used for reducing the search space list, indexing and generating statistical reports etc In this paper, a robust multimodal gender identification method based on the deep features are computed using the off-the-shelf pre-trained deep convolution neural network architecture based on AlexNet. The proposed model consists of 20 subsequent layers which contain different window size of convolutional layers following with fully connected layers for feature extraction and classification. Extensive experiments have been conducted on a homologous SDUMLA-HMT (Shandong University Group of Machine Learning and Applications) multimodal database with 15052 images. The proposed method achieved the accuracy of 99.9% which outperforms the results noticed in the literature.

DOI 10.11648/j.ijdsa.20200602.11
Published in International Journal of Data Science and Analysis ( Volume 6, Issue 2, April 2020 )

This article belongs to the Special Issue Multimodal Biometric Data Analysis

Page(s) 64-68
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

AlexNet, Biometrics, Convolutional Neural Network, Deep Neural Network, Gender Estimation, Multimodal, SDUMLA-HMT

References
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    Shivanand Sharanappa Gornale, Abhijit Patil, Kruti Ramchandra. (2020). Multimodal Biometrics Data Analysis for Gender Estimation Using Deep Learning. International Journal of Data Science and Analysis, 6(2), 64-68. https://doi.org/10.11648/j.ijdsa.20200602.11

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    ACS Style

    Shivanand Sharanappa Gornale; Abhijit Patil; Kruti Ramchandra. Multimodal Biometrics Data Analysis for Gender Estimation Using Deep Learning. Int. J. Data Sci. Anal. 2020, 6(2), 64-68. doi: 10.11648/j.ijdsa.20200602.11

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    AMA Style

    Shivanand Sharanappa Gornale, Abhijit Patil, Kruti Ramchandra. Multimodal Biometrics Data Analysis for Gender Estimation Using Deep Learning. Int J Data Sci Anal. 2020;6(2):64-68. doi: 10.11648/j.ijdsa.20200602.11

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  • @article{10.11648/j.ijdsa.20200602.11,
      author = {Shivanand Sharanappa Gornale and Abhijit Patil and Kruti Ramchandra},
      title = {Multimodal Biometrics Data Analysis for Gender Estimation Using Deep Learning},
      journal = {International Journal of Data Science and Analysis},
      volume = {6},
      number = {2},
      pages = {64-68},
      doi = {10.11648/j.ijdsa.20200602.11},
      url = {https://doi.org/10.11648/j.ijdsa.20200602.11},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.ijdsa.20200602.11},
      abstract = {In the recent past with the rapid growing technology security problem is ubiquitous to our daily life pertinent to it, now a day the usage of biometrics is becoming inevitable. Correspondingly, the field of biometrics has gained tremendous acceptance because of its individualistic and authentication capabilities. In many practical scenario the multimodal-based gender estimation will helps to increase the security and efficiency of other biometrics system. Likewise, in contrast to it uni-modal biometric, the multimodal biometrics system would be very difficult to spoof because of its multiple distinct biometrics features. Gender identification using biometrics traits are mainly used for reducing the search space list, indexing and generating statistical reports etc In this paper, a robust multimodal gender identification method based on the deep features are computed using the off-the-shelf pre-trained deep convolution neural network architecture based on AlexNet. The proposed model consists of 20 subsequent layers which contain different window size of convolutional layers following with fully connected layers for feature extraction and classification. Extensive experiments have been conducted on a homologous SDUMLA-HMT (Shandong University Group of Machine Learning and Applications) multimodal database with 15052 images. The proposed method achieved the accuracy of 99.9% which outperforms the results noticed in the literature.},
     year = {2020}
    }
    

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    T2  - International Journal of Data Science and Analysis
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    AB  - In the recent past with the rapid growing technology security problem is ubiquitous to our daily life pertinent to it, now a day the usage of biometrics is becoming inevitable. Correspondingly, the field of biometrics has gained tremendous acceptance because of its individualistic and authentication capabilities. In many practical scenario the multimodal-based gender estimation will helps to increase the security and efficiency of other biometrics system. Likewise, in contrast to it uni-modal biometric, the multimodal biometrics system would be very difficult to spoof because of its multiple distinct biometrics features. Gender identification using biometrics traits are mainly used for reducing the search space list, indexing and generating statistical reports etc In this paper, a robust multimodal gender identification method based on the deep features are computed using the off-the-shelf pre-trained deep convolution neural network architecture based on AlexNet. The proposed model consists of 20 subsequent layers which contain different window size of convolutional layers following with fully connected layers for feature extraction and classification. Extensive experiments have been conducted on a homologous SDUMLA-HMT (Shandong University Group of Machine Learning and Applications) multimodal database with 15052 images. The proposed method achieved the accuracy of 99.9% which outperforms the results noticed in the literature.
    VL  - 6
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Author Information
  • Department of Computer Science, Rani Channamma University, Belagavi, Karnataka, India

  • Department of Computer Science, Rani Channamma University, Belagavi, Karnataka, India

  • Department of Computer Science, Jain University, Bangalore, India

  • Section