# A simple approach for biometrics: Finger-knuckle prints recognition based on a Sobel filter and similarity measures

arXiv: https://arxiv.org/abs/2605.17673 · HTML: https://mlsanity.com/paper/2605.17673/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 13 references that are in the map.
- First posted on arXiv 17 May 2026.

## Builds on (most cited first, 1 of 1)

- [A novel approach for the automated segmentation and volume quantification of cardiac fats on computed tomography](https://mlsanity.com/paper/2112.11381/) (2021)

## Built on by (most cited first, 5 of 5)

- [Cardiac fat segmentation using computed tomography and an image-to-image conditional generative adversarial neural network](https://mlsanity.com/paper/2605.20064/) (2026)
- [X-Ray cardiac angiographic vessel segmentation based on pixel classification using machine learning and region growing](https://mlsanity.com/paper/2605.20073/) (2026)
- [An Approach for Thyroid Nodule Analysis Using Thermographic Images](https://mlsanity.com/paper/2605.29221/) (2026)
- [Mathematical Morphology in Machine Learning](https://mlsanity.com/paper/2605.30700/) (2026)
- [Iterative Framework For Data Augmentation Of Segmented Fingerprints](https://mlsanity.com/paper/2605.31001/) (2026)

## Abstract (arXiv)

The objective of this work is to propose a novel methodology for the finger knuckle print recognition, which is essentially a digital photo of the finger-knuckle region. We have employed very simple concepts of visual computing such as a filter based on the Sobel operator for finding edges and a simple noise reduction algorithm. These operations are exceptionally fast and produce binary images, which are very efficient to process and to store. Furthermore, alongside this preprocessing, some similarity measures were also regarded and evaluated for the task. After preprocessing an input finger it is compared to all the images of fingers in the dataset, one by one. We have obtained up to 17.02% of successful recognitions (true positive rate) with a large dataset.