Multi-modal biometric authentication: Leveraging shared layer architectures for enhanced security

• URL: http://arxiv.org/abs/2411.02112v1
• Date: Mon, 04 Nov 2024 14:27:10 GMT
• Title: Multi-modal biometric authentication: Leveraging shared layer architectures for enhanced security
• Authors: Vatchala S, Yogesh C, Yeshwanth Govindarajan, Krithik Raja M, Vishal Pramav Amirtha Ganesan, Aashish Vinod A, Dharun Ramesh,
• Abstract summary: We introduce a novel multi-modal biometric authentication system that integrates facial, vocal, and signature data to enhance security measures. Our model architecture incorporates dual shared layers alongside modality-specific enhancements for comprehensive feature extraction. Our approach demonstrates significant improvements in authentication accuracy and robustness, paving the way for advanced secure identity verification solutions.
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• Abstract: In this study, we introduce a novel multi-modal biometric authentication system that integrates facial, vocal, and signature data to enhance security measures. Utilizing a combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), our model architecture uniquely incorporates dual shared layers alongside modality-specific enhancements for comprehensive feature extraction. The system undergoes rigorous training with a joint loss function, optimizing for accuracy across diverse biometric inputs. Feature-level fusion via Principal Component Analysis (PCA) and classification through Gradient Boosting Machines (GBM) further refine the authentication process. Our approach demonstrates significant improvements in authentication accuracy and robustness, paving the way for advanced secure identity verification solutions.

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