Online Signature Recognition: A Biologically Inspired Feature Vector Splitting Approach

• URL: http://arxiv.org/abs/2405.12556v1
• Date: Tue, 21 May 2024 07:51:01 GMT
• Title: Online Signature Recognition: A Biologically Inspired Feature Vector Splitting Approach
• Authors: Marcos Faundez, Moises Diaz, Miguel Angel Ferrer,
• Abstract summary: We employ multiple splitting strategies, aligning with cognitive principles, to preserve control over the relative importance of each feature subset. Experimentation demonstrates that the fusion of pressure data with spatial coordinates (x and y) consistently enhances performance. The inclusion of pen-tip angles in the same feature set yields mixed results, with performance improvements observed in select cases.
• Score: 0.5120567378386615
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This research introduces an innovative approach to explore the cognitive and biologically inspired underpinnings of feature vector splitting for analyzing the significance of different attributes in e-security biometric signature recognition applications. Departing from traditional methods of concatenating features into an extended set, we employ multiple splitting strategies, aligning with cognitive principles, to preserve control over the relative importance of each feature subset. Our methodology is applied to three diverse databases (MCYT100, MCYT300,and SVC) using two classifiers (vector quantization and dynamic time warping with one and five training samples). Experimentation demonstrates that the fusion of pressure data with spatial coordinates (x and y) consistently enhances performance. However, the inclusion of pen-tip angles in the same feature set yields mixed results, with performance improvements observed in select cases. This work delves into the cognitive aspects of feature fusion,shedding light on the cognitive relevance of feature vector splitting in e-security biometric applications.

Related papers

• Feature Decoupling-Recycling Network for Fast Interactive Segmentation [79.22497777645806]
  Recent interactive segmentation methods iteratively take source image, user guidance and previously predicted mask as the input. We propose the Feature Decoupling-Recycling Network (FDRN), which decouples the modeling components based on their intrinsic discrepancies.
  arXiv  Detail & Related papers  (2023-08-07T12:26:34Z)
• Discriminative Co-Saliency and Background Mining Transformer for Co-Salient Object Detection [111.04994415248736]
  We propose a Discriminative co-saliency and background Mining Transformer framework (DMT) We use two types of pre-defined tokens to mine co-saliency and background information via our proposed contrast-induced pixel-to-token correlation and co-saliency token-to-token correlation modules. Experimental results on three benchmark datasets demonstrate the effectiveness of our proposed method.
  arXiv  Detail & Related papers  (2023-04-30T15:56:47Z)
• An Omnidirectional Approach to Touch-based Continuous Authentication [6.83780085440235]
  This paper focuses on how touch interactions on smartphones can provide a continuous user authentication service through behaviour captured by a touchscreen. We present an omnidirectional approach which outperforms the traditional method independent of the touch direction. We find that the TouchAlytics feature set outperforms others when using our approach when combining three or more strokes.
  arXiv  Detail & Related papers  (2023-01-13T13:58:06Z)
• Part-guided Relational Transformers for Fine-grained Visual Recognition [59.20531172172135]
  We propose a framework to learn the discriminative part features and explore correlations with a feature transformation module. Our proposed approach does not rely on additional part branches and reaches state-the-of-art performance on 3-of-the-level object recognition.
  arXiv  Detail & Related papers  (2022-12-28T03:45:56Z)
• GaitStrip: Gait Recognition via Effective Strip-based Feature Representations and Multi-Level Framework [34.397404430838286]
  We present a strip-based multi-level gait recognition network, named GaitStrip, to extract comprehensive gait information at different levels. To be specific, our high-level branch explores the context of gait sequences and our low-level one focuses on detailed posture changes. Our GaitStrip achieves state-of-the-art performance in both normal walking and complex conditions.
  arXiv  Detail & Related papers  (2022-03-08T09:49:48Z)
• Mental State Classification Using Multi-graph Features [9.919882648730164]
  We consider the problem of extracting features from passive, multi-channel electroencephalogram (EEG) devices for downstream inference tasks related to high-level mental states such as stress and cognitive load. Our proposed method leverages recently developed multi-graph tools and applies them to the time series of graphs implied by the statistical dependence structure (e.g., correlation) amongst the multiple sensors. We compare the effectiveness of the proposed features to traditional band power-based features in the context of three classification experiments and find that the two feature sets offer complementary predictive information.
  arXiv  Detail & Related papers  (2022-02-25T19:48:52Z)
• Self-Attention Neural Bag-of-Features [103.70855797025689]
  We build on the recently introduced 2D-Attention and reformulate the attention learning methodology. We propose a joint feature-temporal attention mechanism that learns a joint 2D attention mask highlighting relevant information.
  arXiv  Detail & Related papers  (2022-01-26T17:54:14Z)
• Multivariate feature ranking of gene expression data [62.997667081978825]
  We propose two new multivariate feature ranking methods based on pairwise correlation and pairwise consistency. We statistically prove that the proposed methods outperform the state of the art feature ranking methods Clustering Variation, Chi Squared, Correlation, Information Gain, ReliefF and Significance.
  arXiv  Detail & Related papers  (2021-11-03T17:19:53Z)
• FIVES: Feature Interaction Via Edge Search for Large-Scale Tabular Data [106.76845921324704]
  We propose a novel method named Feature Interaction Via Edge Search (FIVES) FIVES formulates the task of interactive feature generation as searching for edges on the defined feature graph. In this paper, we present our theoretical evidence that motivates us to search for useful interactive features with increasing order.
  arXiv  Detail & Related papers  (2020-07-29T03:33:18Z)
• HDD-Net: Hybrid Detector Descriptor with Mutual Interactive Learning [24.13425816781179]
  Local feature extraction remains an active research area due to the advances in fields such as SLAM, 3D reconstructions, or AR applications. We propose a method that treats both extractions independently and focuses on their interaction in the learning process. We show improvements over the state of the art in terms of image matching on HPatches and 3D reconstruction quality while keeping on par on camera localisation tasks.
  arXiv  Detail & Related papers  (2020-05-12T13:55:04Z)

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.