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Khang Wen Goh

Work place: Faculty of Data Science and Information Technology, INTI International University, 71800 Nilai, Malaysia

E-mail: khangwen.goh@newinti.edu.my

Website:

Research Interests: Computational Mathematics, Machine Learning, Artificial intelligent in learning

Biography

Khang Wen Goh has holds a Ph.D. in Swarm Intelligence with a specialization in Artificial Intelligence. With over 15 years of university teaching experience, he previously served as the Dean of the Faculty of Data Science and Information Technology at INTI International University. Currently, as the Pro Vice Chancellor of Global Engagement at INTI International University, he spearheads the establishment and enhancement of strategic partnerships with leading international higher education institutions. He has published 153 research articles in various National and International Journals and conferences. His area of expertise includes Applied Artificial Intelligence, Agent Based Intelligent Systems, Machine Learning, Applied and Computational Mathematics Computing in Mathematics, Natural Science, Engineering and Medicine, Adaptive Weight Particle Swarm Optimization.

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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