Revealing Reliable Signatures by Learning Top-Rank Pairs
- URL: http://arxiv.org/abs/2203.09927v1
- Date: Thu, 17 Mar 2022 08:20:19 GMT
- Title: Revealing Reliable Signatures by Learning Top-Rank Pairs
- Authors: Xiaotong Ji, Yan Zheng, Daiki Suehiro, Seiichi Uchida
- Abstract summary: Signature verification is a crucial practical documentation analysis task.
We propose a new method to learn "top-rank pairs" for writer-independent offline signature verification tasks.
- Score: 15.582774097442721
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Signature verification, as a crucial practical documentation analysis task,
has been continuously studied by researchers in machine learning and pattern
recognition fields. In specific scenarios like confirming financial documents
and legal instruments, ensuring the absolute reliability of signatures is of
top priority. In this work, we proposed a new method to learn "top-rank pairs"
for writer-independent offline signature verification tasks. By this scheme, it
is possible to maximize the number of absolutely reliable signatures. More
precisely, our method to learn top-rank pairs aims at pushing positive samples
beyond negative samples, after pairing each of them with a genuine reference
signature. In the experiment, BHSig-B and BHSig-H datasets are used for
evaluation, on which the proposed model achieves overwhelming better pos@top
(the ratio of absolute top positive samples to all of the positive samples)
while showing encouraging performance on both Area Under the Curve (AUC) and
accuracy.
Related papers
- Enhanced Bank Check Security: Introducing a Novel Dataset and Transformer-Based Approach for Detection and Verification [11.225067563482169]
We introduce a novel dataset specifically designed for signature verification on bank checks.
This dataset includes a variety of signature styles embedded within typical check elements.
We propose a novel approach for writer-independent signature verification using an object detection network.
arXiv Detail & Related papers (2024-06-20T14:42:14Z) - Uncertainty Aware Learning for Language Model Alignment [97.36361196793929]
We propose uncertainty-aware learning (UAL) to improve the model alignment of different task scenarios.
We implement UAL in a simple fashion -- adaptively setting the label smoothing value of training according to the uncertainty of individual samples.
Experiments on widely used benchmarks demonstrate that our UAL significantly and consistently outperforms standard supervised fine-tuning.
arXiv Detail & Related papers (2024-06-07T11:37:45Z) - One-Shot Learning as Instruction Data Prospector for Large Language Models [108.81681547472138]
textscNuggets uses one-shot learning to select high-quality instruction data from extensive datasets.
We show that instruction tuning with the top 1% of examples curated by textscNuggets substantially outperforms conventional methods employing the entire dataset.
arXiv Detail & Related papers (2023-12-16T03:33:12Z) - Preserving Knowledge Invariance: Rethinking Robustness Evaluation of
Open Information Extraction [50.62245481416744]
We present the first benchmark that simulates the evaluation of open information extraction models in the real world.
We design and annotate a large-scale testbed in which each example is a knowledge-invariant clique.
By further elaborating the robustness metric, a model is judged to be robust if its performance is consistently accurate on the overall cliques.
arXiv Detail & Related papers (2023-05-23T12:05:09Z) - Learning with Signatures [8.569235370614145]
We advance a supervised framework that provides state-of-the-art classification accuracy with the use of very few labels without the need of credit assignment and with minimal or no overfitting.
We leverage tools from harmonic analysis by the use of the signature and log-signature and use as a score function RMSE and MAE Signature and log-signature.
arXiv Detail & Related papers (2022-04-17T08:36:15Z) - Towards Semi-Supervised Deep Facial Expression Recognition with An
Adaptive Confidence Margin [92.76372026435858]
We learn an Adaptive Confidence Margin (Ada-CM) to fully leverage all unlabeled data for semi-supervised deep facial expression recognition.
All unlabeled samples are partitioned into two subsets by comparing their confidence scores with the adaptively learned confidence margin.
Our method achieves state-of-the-art performance, especially surpassing fully-supervised baselines in a semi-supervised manner.
arXiv Detail & Related papers (2022-03-23T11:43:29Z) - SURDS: Self-Supervised Attention-guided Reconstruction and Dual Triplet
Loss for Writer Independent Offline Signature Verification [16.499360910037904]
Offline Signature Verification (OSV) is a fundamental biometric task across various forensic, commercial and legal applications.
We propose a two-stage deep learning framework that leverages self-supervised representation learning as well as metric learning for writer-independent OSV.
The proposed framework has been evaluated on two publicly available offline signature datasets and compared with various state-of-the-art methods.
arXiv Detail & Related papers (2022-01-25T07:26:55Z) - Trash to Treasure: Harvesting OOD Data with Cross-Modal Matching for
Open-Set Semi-Supervised Learning [101.28281124670647]
Open-set semi-supervised learning (open-set SSL) investigates a challenging but practical scenario where out-of-distribution (OOD) samples are contained in the unlabeled data.
We propose a novel training mechanism that could effectively exploit the presence of OOD data for enhanced feature learning.
Our approach substantially lifts the performance on open-set SSL and outperforms the state-of-the-art by a large margin.
arXiv Detail & Related papers (2021-08-12T09:14:44Z) - Provable Guarantees for Self-Supervised Deep Learning with Spectral
Contrastive Loss [72.62029620566925]
Recent works in self-supervised learning have advanced the state-of-the-art by relying on the contrastive learning paradigm.
Our work analyzes contrastive learning without assuming conditional independence of positive pairs.
We propose a loss that performs spectral decomposition on the population augmentation graph and can be succinctly written as a contrastive learning objective.
arXiv Detail & Related papers (2021-06-08T07:41:02Z) - Offline Signature Verification on Real-World Documents [9.271640666465363]
Signatures extracted from formal documents may contain different types of occlusions, for example, stamps, company seals, ruling lines, and signature boxes.
In this paper, we address a real-world writer independent offline signature verification problem, in which, a bank's customers' transaction request documents that contain their occluded signatures are compared with their clean reference signatures.
Our proposed method consists of two main components, a stamp cleaning method based on CycleGAN and signature representation based on CNNs.
arXiv Detail & Related papers (2020-04-25T10:28:03Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.