Feature Fusion-Based Extended Convolutional Ghost Network for Multimodal Biometric Authentication

The process of verifying or identifying individuals based on the combination of two or more biometric modalities is known as multimodal biometric authentication. Multimodal biometric authentication has gained significant attention for enhancing the security of biometrics. Several techniques were developed to perform efficient multimodal biometric authentication. However, previous techniques faced issues in terms of low performance, high error rates, and overfitting problems while handling noisy and imbalanced biometric data. To overcome these issues, a novel optimized feature fusion-based Deep Learning (DL) model for multimodal biometric authentication was developed. The key objective of the proposed framework is to perform multimodal biometric authentication by combining iris, fingerprint, and signature biometrics. The proposed model improves authentication accuracy and reliability by reducing error rates, overfitting, and generalizability across varied biometric modalities. Initially, normalization is used to normalize the image, and Weighted Gaussian Filtering (WGF) is used to reduce the noise. Then, augmentation techniques are applied to solve the imbalance in classes. A Spatial Attention-aided EfficientNet-B0 (SA-EB0) model is utilized to extract essential features by focusing on the most informative sections of each biometric input. A Feature Fusion-based Extended Convolutional Ghost Network (FF-ECGN) is used to fuse the gathered features and also to identify authenticated and unauthenticated data. The feature fusion integrates complementary information obtained from iris, fingerprint, as well as signature to enhance the reliability of authentication. The Chaotic Artificial Humming Bird (CAHB) algorithm is applied to fine-tune model parameters, hence improving the model’s performance. The model’s performance was evaluated through accuracy, precision, recall, [Formula: see text]1-score, specificity, and error metrics MAE, MSE, and RMSE. The proposed approach performs better with 98.55% accuracy and a much lower error rate of 0.0107 EER compared with other existing models.