Noise Resilient Deep Convolutional Neural Networks for Adverse and High Security Applications

Author(s)	Dr. Supreetha Gowda HD, Smt Rachana CR
Country	India
Abstract	Human physiological characteristics serve as highly dependable authentication mech- anisms because of their distinctive, permanent, and tamper-resistant properties in identity verification applications. Within biometric authentication frameworks, the feature extrac- tion process plays a pivotal role in ensuring system compatibility, minimizing incorrect matches, and strengthening security protocols. This research introduces an innovative deep convolutional neural network architecture that demonstrates exceptional noise tolerance while successfully extracting distinguishable biological features despite varying intensities of environmental interference.
Keywords	Computer vision; Biometric verification system; Deep learning; Convolu- tion neural network; Additive noise; Robustness
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 7, Issue 5, September-October 2025
Published On	2025-10-25
DOI	https://doi.org/10.36948/ijfmr.2025.v07i05.58699

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