SM-DTW: Stability Modulated Dynamic Time Warping for signature verification
- URL: http://arxiv.org/abs/2405.11978v1
- Date: Mon, 20 May 2024 12:18:15 GMT
- Title: SM-DTW: Stability Modulated Dynamic Time Warping for signature verification
- Abstract summary: We introduce the concept of stability to explain the difference between the actual movements performed during multiple execution of the subject's signature.
We conjecture that the most stable parts of the signature should play a paramount role in evaluating the similarity between a questioned signature and the reference ones during signature verification.
We introduce the Stability Modulated Dynamic Time Warping algorithm for incorporating the stability regions.
- Score: 4.299840769087444
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Building upon findings in computational model of handwriting learning and execution, we introduce the concept of stability to explain the difference between the actual movements performed during multiple execution of the subject's signature, and conjecture that the most stable parts of the signature should play a paramount role in evaluating the similarity between a questioned signature and the reference ones during signature verification. We then introduce the Stability Modulated Dynamic Time Warping algorithm for incorporating the stability regions, i.e. the most similar parts between two signatures, into the distance measure between a pair of signatures computed by the Dynamic Time Warping for signature verification. Experiments were conducted on two datasets largely adopted for performance evaluation. Experimental results show that the proposed algorithm improves the performance of the baseline system and compares favourably with other top performing signature verification systems.
Related papers
- Skill-Aligned Annotation for Reliable Evaluation in Text-to-Image Generation [50.85337196571056]
Text-to-image (T2I) generation has advanced rapidly, making reliable evaluation critical as performance differences between models narrow.<n>Existing evaluation practices typically apply uniform annotation mechanisms, such as Likert-scale or binary question answering (BQA)<n>We revisit T2I evaluation through the lens of skill-aligned annotation, where annotation strategies reflect the underlying characteristics of each evaluation skill.
arXiv Detail & Related papers (2026-05-13T09:14:31Z) - DynT2I-Eval: A Dynamic Evaluation Framework for Text-to-Image Models [78.62380562116135]
We propose DynT2I-Eval, a fully automated dynamic evaluation framework for text-to-image (T2I) models.<n>It constructs a structured visual semantic space from long-form descriptions, decomposing prompts into controllable dimensions.<n>DynT2I-Eval evaluates model performance across text alignment, perceptual quality, and aesthetics.
arXiv Detail & Related papers (2026-05-07T12:53:51Z) - Multimodal Learning on Low-Quality Data with Conformal Predictive Self-Calibration [72.0672328514289]
Multimodal learning often grapples with the challenge of low-quality data.<n>We propose a unified framework, termed Conformal Predictive Self-Calibration.<n>Our framework consistently outperforms existing state-of-the-art methods.
arXiv Detail & Related papers (2026-05-05T14:48:52Z) - Behavioral Fingerprints for LLM Endpoint Stability and Identity [6.5886705394788665]
Stability Monitor periodically fingerprints an endpoint by sampling outputs from a fixed prompt set.<n>In controlled validation, Stability Monitor detects changes to model family, version, inference stack, quantization, and behavioral parameters.<n>In real-world monitoring of the same model hosted by multiple providers, we observe substantial provider-to-provider and within-provider stability differences.
arXiv Detail & Related papers (2026-03-19T15:23:23Z) - Online Handwritten Signature Verification Based on Temporal-Spatial Graph Attention Transformer [24.79683227958907]
This paper introduces a novel approach for dynamic signature verification: the Temporal-Spatial Graph Attention Transformer (TS-GATR)<n>TS-GATR combines the Graph Attention Network (GAT) and the Gated Recurrent Unit (GRU) to model both spatial and temporal dependencies in signature data.<n>To capture long-term temporal dependencies, the model integrates GRU, thereby enhancing its ability to learn dynamic features during signature verification.
arXiv Detail & Related papers (2025-10-22T07:32:55Z) - Signature Forgery Detection: Improving Cross-Dataset Generalization [0.0]
Signature verification is a critical biometric technique used in banking, identity authentication, and legal documentation.<n>Most approaches in offline signature verification still struggle to generalize across datasets.<n>This study investigates feature learning strategies for signature forgery detection.
arXiv Detail & Related papers (2025-10-20T16:42:21Z) - Prompt Stability in Code LLMs: Measuring Sensitivity across Emotion- and Personality-Driven Variations [40.12950482269347]
We present PromptSE, a framework that creates semantically equivalent prompt variants with emotion and personality templates.<n>Our study shows that performance and stability behave as largely decoupled optimization objectives.<n>PromptSE enables practitioners to quantify performance stability trade offs for deployment and model selection.
arXiv Detail & Related papers (2025-09-17T04:17:42Z) - Capturing More: Learning Multi-Domain Representations for Robust Online Handwriting Verification [49.085301457166544]
SPECTRUM is a temporal-frequency synergistic model that unlocks the untapped potential of multi-domain representation learning for online handwriting verification (OHV)<n>Extensive experiments demonstrate SPECTRUM's superior performance over existing methods.<n>These findings pave the way for future research in multi-domain approaches across both feature and biometric domains.
arXiv Detail & Related papers (2025-08-02T16:20:35Z) - An Improved U-Net Model for Offline handwriting signature denoising [0.0]
In forensic science appraisals, the analysis of offline handwriting signatures requires the appraiser to provide a certain number of signature samples.<n>The provided handwriting samples are often mixed with a large amount of interfering information, which brings severe challenges to handwriting identification work.<n>This study proposes a signature handwriting denoising model based on the improved U-net structure, aiming to enhance the robustness of the signature recognition system.
arXiv Detail & Related papers (2025-07-01T01:38:44Z) - Offline Handwritten Signature Verification Using a Stream-Based Approach [7.18805896964466]
Signature verification systems distinguish between genuine and forged signatures.
Traditional HSV development involves a static batch configuration.
We propose a novel HSV approach with an adaptive system that receives an infinite sequence of signatures and is updated over time.
arXiv Detail & Related papers (2024-11-10T16:16:06Z) - Analogous Alignments: Digital "Formally" meets Analog [0.0]
This paper focuses on the pragmatic formal verification of a mixed signal Intellectual Property (IP) that has a combination of digital and analog blocks.
Digital and Analog Mixed-Signal (AMS) designs, which are fundamentally different in nature, are integrated seamlessly in a formal verification setup.
arXiv Detail & Related papers (2024-09-23T13:38:31Z) - 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) - Graph-Aware Contrasting for Multivariate Time-Series Classification [50.84488941336865]
Existing contrastive learning methods mainly focus on achieving temporal consistency with temporal augmentation and contrasting techniques.
We propose Graph-Aware Contrasting for spatial consistency across MTS data.
Our proposed method achieves state-of-the-art performance on various MTS classification tasks.
arXiv Detail & Related papers (2023-09-11T02:35:22Z) - Self-Sufficient Framework for Continuous Sign Language Recognition [75.60327502570242]
The goal of this work is to develop self-sufficient framework for Continuous Sign Language Recognition.
These include the need for complex multi-scale features such as hands, face, and mouth for understanding, and absence of frame-level annotations.
We propose Divide and Focus Convolution (DFConv) which extracts both manual and non-manual features without the need for additional networks or annotations.
DPLR propagates non-spiky frame-level pseudo-labels by combining the ground truth gloss sequence labels with the predicted sequence.
arXiv Detail & Related papers (2023-03-21T11:42:57Z) - Fine-grained Temporal Contrastive Learning for Weakly-supervised
Temporal Action Localization [87.47977407022492]
This paper argues that learning by contextually comparing sequence-to-sequence distinctions offers an essential inductive bias in weakly-supervised action localization.
Under a differentiable dynamic programming formulation, two complementary contrastive objectives are designed, including Fine-grained Sequence Distance (FSD) contrasting and Longest Common Subsequence (LCS) contrasting.
Our method achieves state-of-the-art performance on two popular benchmarks.
arXiv Detail & Related papers (2022-03-31T05:13:50Z) - Revealing Reliable Signatures by Learning Top-Rank Pairs [15.582774097442721]
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.
arXiv Detail & Related papers (2022-03-17T08:20:19Z) - Conformance Checking Over Stochastically Known Logs [7.882975068446842]
Data logs may become uncertain due to, e.g., sensor reading inaccuracies or incorrect interpretation of readings by processing programs.
In this work we focus on conformance checking, which compares a process model with an event log.
We mathematically define a trace model, a synchronous product, and a cost function that reflects the uncertainty of events in a log.
arXiv Detail & Related papers (2022-03-14T21:33:06Z) - Deep Explicit Duration Switching Models for Time Series [84.33678003781908]
We propose a flexible model that is capable of identifying both state- and time-dependent switching dynamics.
State-dependent switching is enabled by a recurrent state-to-switch connection.
An explicit duration count variable is used to improve the time-dependent switching behavior.
arXiv Detail & Related papers (2021-10-26T17:35:21Z) - Inter-class Discrepancy Alignment for Face Recognition [55.578063356210144]
We propose a unified framework calledInter-class DiscrepancyAlignment(IDA)
IDA-DAO is used to align the similarity scores considering the discrepancy between the images and its neighbors.
IDA-SSE can provide convincing inter-class neighbors by introducing virtual candidate images generated with GAN.
arXiv Detail & Related papers (2021-03-02T08:20:08Z) - Towards Model-Agnostic Post-Hoc Adjustment for Balancing Ranking
Fairness and Algorithm Utility [54.179859639868646]
Bipartite ranking aims to learn a scoring function that ranks positive individuals higher than negative ones from labeled data.
There have been rising concerns on whether the learned scoring function can cause systematic disparity across different protected groups.
We propose a model post-processing framework for balancing them in the bipartite ranking scenario.
arXiv Detail & Related papers (2020-06-15T10:08:39Z)
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.