The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
SIGNATURE VERIFICATION USING A “SIAMESE” TIME DELAY NEURAL NETWORK
Abstract
This paper describes an algorithm for verification of signatures written on a pen-input tablet. The algorithm is based on a novel, artificial neural network, called a "Siamese" neural network. This network consists of two identical sub-networks joined at their outputs. During training the two sub-networks extract features from two signatures, while the joining neuron measures the distance between the two feature vectors. Verification consists of comparing an extracted feature vector with a stored feature vector for the signer. Signatures closer to this stored representation than a chosen threshold are excepted, all other signatures are rejected as forgeries.
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“Most of the image registration methods using deep learning are based on a Siamese network (Bromley et al., 1994;Merkle et al., 2017;He et al., 2018;Hughes et al., 2018a;Wang et al., 2018b).”
From Deep learning in remote sensing applications: A meta-analysis and review (2019) · cites this paper as Bromley et al., 1994
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Metadata
- Authors
- BROMLEY, J., BENTZ, J. W., BOTTOU, L., GUYON, I., LECUN, Y., MOORE, C., SÄCKINGER, E., & SHAH, R.
- Affiliations
- AT&T (United States) (US)
- Venue
- Series in Machine Perception and Artificial Intelligence
- Publisher
- WORLD SCIENTIFIC
- Published
- 1994-01
- Pages
- 25-44
- ISSN
- 1793-0839 (ISSN-L)
- OpenAlex
- W2127589108
- Cited by
- 2,350
- References
- 8
- Citations by year
- 2026: 47 · 2025: 148 · 2024: 177 · 2023: 218 · 2022: 212 · 2021: 350 · 2020: 409 · 2019: 339 · 2018: 194 · 2017: 100 · 2016: 68 · 2015: 36 · 2014: 8 · 2013: 4 · 2012: 6
- FWCI
- 0.85
- Type
- book chapter
- Language
- en
- Open access
- closed
- Locations
- Series in machine perception and artificial intelligence (publishedVersion)
- Open Research Online (The Open University) (acceptedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Handwritten Text Recognition Techniques
- Physical Sciences › Computer Science › Information Systems › User Authentication and Security Systems
- Physical Sciences › Computer Science › Signal Processing › Biometric Identification and Security
- Keywords
- signature verification, Siamese network, time-delay neural networks, feature extraction, forgery detection, biometric authentication
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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