A simple approach for biometrics: Finger-knuckle prints recognition based on a Sobel filter and similarity measures
This work addresses biometric recognition for finger-knuckle prints, but the performance is very low and likely not competitive with existing methods.
The paper proposes a simple finger-knuckle print recognition method using Sobel filter and similarity measures, achieving up to 17.02% true positive rate on a large dataset.
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.