# Study of a committee of neural networks for biometric hand-geometry recognition

arXiv: https://arxiv.org/abs/2204.03935 · HTML: https://mlsanity.com/paper/2204.03935/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 18 references that are in the map.
- First posted on arXiv 8 Apr 2022.

## Builds on (most cited first, 4 of 4)

- [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)

## Built on by (most cited first, 1 of 1)

- [Biometric verification of humans by means of hand geometry](https://mlsanity.com/paper/2204.07764/) (2022)

## Abstract (arXiv)

This Paper studies different committees of neural networks for biometric pattern recognition. We use the neural nets as classifiers for identification and verification purposes. We show that a committee of nets can improve the recognition rates when compared with a multi-start initialization algo-rithm that just picks up the neural net which offers the best performance. On the other hand, we found that there is no strong correlation between identifi-cation and verification applications using the same classifier.