# Biometric verification of humans by means of hand geometry

arXiv: https://arxiv.org/abs/2204.07764 · HTML: https://mlsanity.com/paper/2204.07764/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 21 references that are in the map.
- First posted on arXiv 16 Apr 2022.

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

- [Privacy issues on biometric systems](https://mlsanity.com/paper/2202.11415/) (2022)
- [Technological evaluation of two AFIS systems](https://mlsanity.com/paper/2203.00447/) (2022)
- [Biometric recognition: why not massively adopted yet?](https://mlsanity.com/paper/2203.03719/) (2022)
- [Testing report of a fingerprint-based door-opening system](https://mlsanity.com/paper/2202.11419/) (2022)
- [Biometric identification by means of hand geometry and a neural net classifier](https://mlsanity.com/paper/2204.03925/) (2022)
- [Study of a committee of neural networks for biometric hand-geometry recognition](https://mlsanity.com/paper/2204.03935/) (2022)

## Abstract (arXiv)

This paper describes a hand geometry biometric identification system. We have acquired a database of 22 people, 10 acquisitions per person, using a conventional document scanner. We propose a feature extraction and classifier. The experimental results reveal a maximum identification rate equal to 93.64%, and a minimum value of the detection cost function equal to 2.92% using a multi layer perceptron classifier.