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Shivanand Gornale

Work place: Department of Computer Science, Rani Channamma University, Belagavi-591156, Karnataka, India

E-mail: shivanand1971@gmail.com

Website:

Research Interests: Graph and Image Processing, Computer Vision, Machine Learning, Pattern Recognition, Image Processing

Biography

Prof. Shivanand Gornale has completed M. Sc. in Computer Science. M.Phil. in Computer Science., Ph.D. in Computer Science from Savitribai Phule Pune University, Maharashtra, India in 2009 under the guidance of Professor. K V Kale and has been recognized as a research guide for Ph.D. in Computer Science and Engineering from Rani Channamma University, Belagavi and Jain University Bangalore. He has published more than 100+ research papers in various National and International Journals and conferences. He is a Fellow of IETE New Delhi, Life Member of CSI, Life Member of Indian Unit of Pattern Recognition and Artificial Intelligence (IPRA), Member of Indian Association for Research in Computer Science (IARCS), Member of International Association of Computer Science and Information Technology (IACS&IT) Singapore, Member of International Association for Engineers’, Hong Kong, Member of Computer Science Teachers Association, USA, Life Member of Indian Science Congress Association, Kolkata-India. Presently he is working as Professor, Department of Computer Science. His research areas of interest are Digital Image Processing, Pattern Recognition, Computer Vision and Machine Learning, Video Retrieval and Biometric analysis.

Author Articles
Fingerprint Image Fusion: A Cutting-edge Perspective on Gender Classification via Rotational Invariant Features

By Shivanand Gornale Abhijit Patil Khang Wen Goh Sathish Kumar Kruthi R

DOI: https://doi.org/10.5815/ijigsp.2024.04.04, Pub. Date: 8 Aug. 2024

In this cutting-edge technological milieu, fingerprints have become an alternative expression for the biometrics system. A fingerprint is one of the perceptible biometric modals which is predominantly utilized in almost all the security, and real-life applications. Fingerprints have many inherent rotational features that are mostly utilized for person recognition besides these features can also be utilized for the person gender classification. Thus, the proposed work is a novel algorithm which identifies the gender of an individual based on the fingerprint. The image fusion and feature level fusion technique are deliberated over the fingerprints with rotational invariant features. Experiments were carried on four state-of-the-art datasets and realized promising results by outperforming earlier outcomes.

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