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

TL;DR

This paper presents a multi-modal biometric authentication system that combines facial, vocal, and signature data using shared layer architectures and advanced machine learning techniques to improve security and accuracy.

Contribution

It introduces a novel architecture with shared layers and modality-specific enhancements for multi-modal biometric authentication, improving upon existing methods.

Findings

Enhanced authentication accuracy and robustness

Effective feature fusion via PCA

Improved security in identity verification

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.