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Home / Journals / International Journal of Data Science and Analysis / Multimodal Biometric Data Analysis
Multimodal Biometric Data Analysis

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Lead Guest Editor:
Dr. Shivanand Gornale
Department of Computer Science, School of Mathematics and Computing Sciences, Rani Channamma University, Belagavi, Karnataka, 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
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
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