Fusion Net-3: Denoising-based secure biometric authentication using fingerprints
Fingerprint-based authentication is a critical biometric approach for ensuring security and accuracy. Traditional methods often face challenges such as noise and suboptimal feature extraction. To address the challenges, Fusion Net-3, an extensive model, is proposed to improve the speed, precision, and security level of fingerprint-based authentication systems. Fusion Net-3 operates through two separate stages: enrollment and authentication. During the enrollment phase, advanced pre-processing of fingerprint images was performed, incorporating an enhanced bilateral filter optimized with the seagull optimization algorithm. After pre-processing, features were obtained using a two-phase method: Zernike moments for shape-based features and local binary patterns for texture-based features. This helped ensure that fingerprint features were considered comprehensive for representation. For feature selection optimization, the falcon-inspired jackal optimization algorithm was proposed, a hybrid method combining the strengths of the golden jackal optimization and falcon optimization algorithm. Then, the selected features were combined using a combination of the geometric mean and the Fisher score to facilitate classification for a balanced and novel representation. During authentication, fingerprints were processed using similar techniques for consistency. Each fingerprint was labeled as genuine or fraudulent with the aid of the Fusion Net-3 model, which leverages the combined strengths of convolutional neural networks, ResNet-50, and U-Net. The model achieved an accuracy of 98.956% and a mean squared error of 0.0234 when implemented on a Python platform. Overall, the Fusion Net-3 model demonstrated superior performance compared to existing methods, effectively enhancing authentication accuracy and security.
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