A biometric system based on Gabor feature extraction with SVM classifier for Finger-Knuckle-Print

Abstract

An authentic Personal identification infrastructure helps to control the access in order to secure data and information. Biometric technology is mainly based on physiological or behavioural characteristics of human body. This paper elucidates Finger Knuckle Print (FKP) biometric system based on feature extraction methodology using the short and long Gabor feature extraction. This FKP authentication system involves all basic processes like pre-processing, feature extraction and classification. This feature extraction is done by Gabor filter for extracting the important features form the FKP dataset. The query FKP Gabor features are matched and compared with the enrolled template using Hamming distance [HD]. Finally this paper proposes the FKP recognition based on Support Vector Machines in accordance with score level fusion to improve the recognition performance of FKP by integrating the Gabor features. The main aim of this paper is to utilize the ability of Support Vector Machines (SVM) in pattern recognition and classifying with Hamming distance which helps to improve the False Acceptance Rate (FAR) and Genuine Acceptance Rate (GAR). This new combination (double instance) of FKP shows better results as 96.01% for MAX Rule and 92.33% for Min Rule than single instance performance as 89.11% .This idea shows good results in Finger Knuckle Print recognition of a person.