Summary of Consensus-threshold Criterion For Offline Signature Verification Using Convolutional Neural Network Learned Representations, by Paul Brimoh et al.
Consensus-Threshold Criterion for Offline Signature Verification using Convolutional Neural Network Learned Representations
by Paul Brimoh, Chollette C. Olisah
First submitted to arxiv on: 5 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 proposed consensus-threshold distance-based classifier criterion for offline writer-dependent signature verification improves the state-of-the-art performance by minimizing the false acceptance rate (FAR). The method leverages features extracted from SigNet and SigNet-F deep convolutional neural network models. Experiments on four datasets, including GPDS-300, MCYT, CEDAR, and Brazilian PUC-PR, demonstrate the effectiveness of the proposed classifier in achieving a 1.27% FAR compared to existing methods. This performance is consistent across all datasets, ensuring that the risk of imposters gaining access to sensitive documents or transactions is minimal. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Signature verification is crucial for preventing false acceptance rates (FAR) when verifying signatures. Existing methods have high misclassification errors, making it difficult to determine if a signature is genuine or forged. The proposed consensus-threshold distance-based classifier criterion solves this problem by minimizing FAR using features extracted from deep neural network models. This method can be used in various applications where secure document transactions are necessary. |
Keywords
* Artificial intelligence * Neural network
