Summary of Multi-modal Biometric Authentication: Leveraging Shared Layer Architectures For Enhanced Security, by Vatchala S et al.
Multi-modal biometric authentication: Leveraging shared layer architectures for enhanced security
by Vatchala S, Yogesh C, Yeshwanth Govindarajan, Krithik Raja M, Vishal Pramav Amirtha Ganesan, Aashish Vinod A, Dharun Ramesh
First submitted to arxiv on: 4 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The novel multi-modal biometric authentication system combines facial, vocal, and signature data to enhance security measures. The model architecture utilizes Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), with dual shared layers and modality-specific enhancements for feature extraction. The system is trained with a joint loss function, optimizing accuracy across diverse biometric inputs. Feature-level fusion via Principal Component Analysis (PCA) and classification through Gradient Boosting Machines (GBM) refine the authentication process. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to keep things secure by combining different types of data, like facial expressions, voices, and handwriting. The system uses special kinds of artificial intelligence called neural networks to analyze this information and make sure it’s really you trying to get in. It does this better than other methods because it looks at all the different types of data together, not just one or two. |
Keywords
» Artificial intelligence » Boosting » Classification » Feature extraction » Loss function » Multi modal » Pca » Principal component analysis
