# Online Signature Recognition: A Biologically Inspired Feature Vector Splitting Approach

arXiv: https://arxiv.org/abs/2405.12556 · HTML: https://mlsanity.com/paper/2405.12556/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 39 references that are in the map.
- First posted on arXiv 21 May 2024.

## Builds on (most cited first, 6 of 6)

- [BiosecurID: a multimodal biometric database](https://mlsanity.com/paper/2111.03472/) (2021)
- [Handwriting Biometrics: Applications and Future Trends in e-Security and e-Health](https://mlsanity.com/paper/2202.12760/) (2022)
- [Online handwriting, signature and touch dynamics: tasks and potential applications in the field of security and health](https://mlsanity.com/paper/2202.12693/) (2022)
- [Biometric security technology](https://mlsanity.com/paper/2202.11459/) (2022)
- [Sensor interoperability and fusion in signature verification: A case study using tablet PC](https://mlsanity.com/paper/2210.11139/) (2022)
- [SM-DTW: Stability Modulated Dynamic Time Warping for signature verification](https://mlsanity.com/paper/2405.11978/) (2024)

## Abstract (arXiv)

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.