International Journal of Data Science and Analysis

Special Issue

Multimodal Biometric Data Analysis

  • Submission Deadline: Apr. 10, 2020
  • Status: Submission Closed
  • Lead Guest Editor: Dr. Shivanand Gornale
About This Special Issue
Establishing the identity and femininity of a person is becoming critical in our vastly interconnected society. Is it really you who you are claiming to be? Or is it really that you are not the person who you are claiming not to be? The need for reliable user authentication and gender identification techniques have increased in the wake of heightened concerns about security and rapid advancement in Networking and human computer interactions.
Most biometric systems deployed in real world applications are Uni-modal, i.e., single finger print, or a face or an iris, etc. These systems have to contend with a variety of limitations and problems that you can face when applying Uni-modal Biometric system. The problems such as noisy data, intra-class variations, restricted degree of freedom, non-universality, spoof attacks, and unacceptable error rates, can be addressed by deploying a multimodal biometric system.
There are various scenarios that are possible in multimodal biometric data analysis, and level of fusion that are probable and the integration strategies that can be adopted to consolidate information.
This special issue aims to contribute to the explanation of how Multimodal Biometric Data Analysis can help Data Science and Analysis for reliable user authentication and gender identification.

Aims and Scope:

  1. Fusion and Privacy in Biometrics
  2. Multimodal Biometrics Data Analysis
  3. Data Analysis for Forensic Applications
  4. Biometric Models improvement
  5. Machine Vision Approach for Biometric Data Analysis
  6. Soft Biometric Analysis
Lead Guest Editor
  • Dr. Shivanand Gornale

    Department of Computer Science, School of Mathematics and Computing Sciences, Rani Channamma University, Belagavi, India

Guest Editors
  • Ramesh Manza

    Department of Computer Science,Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, India

  • Shivashankar S

    Department of Computer Science,Karnataka Univeristy, Dharwad, India

  • Aziz Makandar

    Department of Computer Science,Karnataka State Women’s University, Bijapur, India

  • Mallikarjun Hangarge

    Department of Computer Science,Karnataka Arts Science and Commerce College, Bidar, India

  • Vikas Humbe

    Department of Computer Science,Swami Ramanand Teerth Maratwada University, Nanded, India

  • Majharoddin Kazi

    Department of Computer Science and Applications,MGM's Dr. G.Y. Pathrikar College of Computer Science and Information Technology, Auranagabad, India

  • Dnyaneshwari Patil

    Department of Computer Science and It., Dr. G. Y. Pathrikar College of CS&IT, Dr. BAMU. Aurangabad, Aurangabad, India

Published Articles
  • Multimodal Biometrics Data Analysis for Gender Estimation Using Deep Learning

    Shivanand Sharanappa Gornale , Abhijit Patil , Kruti Ramchandra

    Issue: Volume 6, Issue 2, April 2020
    Pages: 64-68
    Received: Dec. 09, 2019
    Accepted: Dec. 16, 2019
    Published: May 29, 2020
    DOI: 10.11648/j.ijdsa.20200602.11
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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 mult... Show More