Gabor, LBP, and BSIF features: Which is more appropriate for finger-knuckles-print recognition?

An accurate personal identification system helps control access to secure information and data. Biometric technology mainly focuses on
the physiological or behavioural characteristics of the human body. This paper investigates the Finger Knuckle Print (FKP) biometric device based
on the feature extraction technique. This FKP authentication method includes all the essential processes, such as preprocessing, feature extraction
and classification. The features of the FKP application are investigated. Finally, this paper proposes the selection of the best feature extraction
based on FKP recognition efficiency. The primary purpose of this paper is to use the Local Binary Patterns (LBP), Binarized Statistical Image
Features (BSIF), and Gabor filters and define which helps to increase the False Acceptability Rate (FAR) and Genuine Acceptability Rate (GAR).
This latest FKP selection shows better results as this concept shows promising results in recognizing a person’s fingerknuckle print.