Offline Handwritten Signature Verification Using a Stream-Based Approach

• URL: http://arxiv.org/abs/2411.06510v1
• Date: Sun, 10 Nov 2024 16:16:06 GMT
• Title: Offline Handwritten Signature Verification Using a Stream-Based Approach
• Authors: Kecia G. de Moura, Rafael M. O. Cruz, Robert Sabourin,
• Abstract summary: 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.
• Score: 7.18805896964466
• License:
• Abstract: Handwritten Signature Verification (HSV) systems distinguish between genuine and forged signatures. Traditional HSV development involves a static batch configuration, constraining the system's ability to model signatures to the limited data available. Signatures exhibit high intra-class variability and are sensitive to various factors, including time and external influences, imparting them a dynamic nature. This paper investigates the signature learning process within a data stream context. We propose a novel HSV approach with an adaptive system that receives an infinite sequence of signatures and is updated over time. Experiments were carried out on GPDS Synthetic, CEDAR, and MCYT datasets. Results demonstrate the superior performance of the proposed method compared to standard approaches that use a Support Vector Machine as a classifier. Implementation of the method is available at https://github.com/kdMoura/stream_hsv.

Related papers

• Offline Signature Verification Based on Feature Disentangling Aided Variational Autoencoder [6.128256936054622]
  Main tasks of signature verification systems include extracting features from signature images and training a classifier for classification. The instances of skilled forgeries are often unavailable, when signature verification models are being trained. This paper proposes a new signature verification method using a variational autoencoder (VAE) to extract features directly from signature images.
  arXiv  Detail & Related papers  (2024-09-29T19:54:47Z)
• DiffusionPen: Towards Controlling the Style of Handwritten Text Generation [7.398476020996681]
  DiffusionPen (DiffPen) is a 5-shot style handwritten text generation approach based on Latent Diffusion Models. Our approach captures both textual and stylistic characteristics of seen and unseen words and styles, generating realistic handwritten samples. Our method outperforms existing methods qualitatively and quantitatively, and its additional generated data can improve the performance of Handwriting Text Recognition (HTR) systems.
  arXiv  Detail & Related papers  (2024-09-09T20:58:25Z)
• SM-DTW: Stability Modulated Dynamic Time Warping for signature verification [4.299840769087444]
  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.
  arXiv  Detail & Related papers  (2024-05-20T12:18:15Z)
• Online Feature Updates Improve Online (Generalized) Label Shift Adaptation [51.328801874640675]
  Our novel method, Online Label Shift adaptation with Online Feature Updates (OLS-OFU), leverages self-supervised learning to refine the feature extraction process. By carefully designing the algorithm, OLS-OFU maintains the similar online regret convergence to the results in the literature while taking the improved features into account.
  arXiv  Detail & Related papers  (2024-02-05T22:03:25Z)
• Semi-Supervised Class-Agnostic Motion Prediction with Pseudo Label Regeneration and BEVMix [59.55173022987071]
  We study the potential of semi-supervised learning for class-agnostic motion prediction. Our framework adopts a consistency-based self-training paradigm, enabling the model to learn from unlabeled data. Our method exhibits comparable performance to weakly and some fully supervised methods.
  arXiv  Detail & Related papers  (2023-12-13T09:32:50Z)
• Towards General Visual-Linguistic Face Forgery Detection [95.73987327101143]
  Deepfakes are realistic face manipulations that can pose serious threats to security, privacy, and trust. Existing methods mostly treat this task as binary classification, which uses digital labels or mask signals to train the detection model. We propose a novel paradigm named Visual-Linguistic Face Forgery Detection(VLFFD), which uses fine-grained sentence-level prompts as the annotation.
  arXiv  Detail & Related papers  (2023-07-31T10:22:33Z)
• Diffusion-Based Representation Learning [65.55681678004038]
  We augment the denoising score matching framework to enable representation learning without any supervised signal. In contrast, the introduced diffusion-based representation learning relies on a new formulation of the denoising score matching objective. Using the same approach, we propose to learn an infinite-dimensional latent code that achieves improvements of state-of-the-art models on semi-supervised image classification.
  arXiv  Detail & Related papers  (2021-05-29T09:26:02Z)
• Autoencoding Variational Autoencoder [56.05008520271406]
  We study the implications of this behaviour on the learned representations and also the consequences of fixing it by introducing a notion of self consistency. We show that encoders trained with our self-consistency approach lead to representations that are robust (insensitive) to perturbations in the input introduced by adversarial attacks.
  arXiv  Detail & Related papers  (2020-12-07T14:16:14Z)
• Intrapersonal Parameter Optimization for Offline Handwritten Signature Augmentation [17.11525750244627]
  We propose a method to automatically model the most common writer variability traits. The method is used to generate offline signatures in the image and the feature space and train an ASVS. We evaluate the performance of an ASVS with the generated samples using three well-known offline signature datasets.
  arXiv  Detail & Related papers  (2020-10-13T19:54:02Z)
• FCN+RL: A Fully Convolutional Network followed by Refinement Layers to Offline Handwritten Signature Segmentation [3.3144312096837325]
  We propose an approach to locate and extract the pixels of handwritten signatures on identification documents. The technique is based on a fully convolutional encoder-decoder network combined with a block of refinement layers for the alpha channel of the predicted image.
  arXiv  Detail & Related papers  (2020-05-28T18:47:10Z)
• POINTER: Constrained Progressive Text Generation via Insertion-based Generative Pre-training [93.79766670391618]
  We present POINTER, a novel insertion-based approach for hard-constrained text generation. The proposed method operates by progressively inserting new tokens between existing tokens in a parallel manner. The resulting coarse-to-fine hierarchy makes the generation process intuitive and interpretable.
  arXiv  Detail & Related papers  (2020-05-01T18:11:54Z)

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.