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
• Authors: Antonio Parziale, Moises Diaz, Miguel A. Ferrer, Angelo Marcelli,
• 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

• 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.