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

TL;DR

This paper introduces a fast, simple finger-knuckle print recognition method using Sobel filtering and similarity measures, achieving up to 17.02% true positive rate on a large dataset.

Contribution

The work presents a novel, computationally efficient approach for finger-knuckle recognition combining edge detection and similarity metrics.

Findings

Achieved up to 17.02% true positive rate on a large dataset.

Utilized simple Sobel filter and noise reduction for fast processing.

Demonstrated effectiveness of basic visual computing techniques for biometric recognition.

Abstract

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.