Abstract
Objective: To extract the Region of Interest (ROI) of palmprint image by using appropriate methods and to improve the accuracy of palmprint recognition system. Methods/Statistical Analysis: This piece of work is primarily addressing the different mechanisms for extracting ROI area. The techniques like Competitive Hand Valley Detection (CHVD), and Euclidean Distance (ED) were applied as the part of pre-processing, while the Feature Extraction mechanism LBP was utilized to extract the texture feature from different type of ROIs of palmprint image. Findings: The experimental results showed that CHVD with LBP gave best result with high accuracy reached to 96.10534% and Equal Error Rate (EER) of 3.894661%, while in ED the best result showed accuracy reached to 88.23611% and EER of 11.76389%. Application/Improvements: The study mainly concentrated on developing palmprint authentication system with less EER and high accuracy. Keywords: Analysis, Competitive Hand Valley Detection (CHVD), Euclidean Distance, Local Binary Pattern (LBP), Palmprint Recognition
Highlights
Today, personal identification playsan active research and applications in our daily life like banking, immigration, and access control, ID card, passport office and country borders etc
The system can achieved the best performance with minimum Equal Error Rate (EER) and maximum Genuine Accept Rate (GAR) which determined with help of threshold values
The comparison between both Region of Interest (ROI) methods shows that Euclidean Distance (ED) with Local Binary Pattern (LBP) feature technique gave the least result with higher EER reached to 11.76389% and GAR of 88.23611% while Competitive Hand Valley Detection (CHVD) with LBP achieved the best result with minimum EER of 3.894661% and maximum GAR of 96.10534%
Summary
Personal identification playsan active research and applications in our daily life like banking, immigration, and access control, ID card, passport office and country borders etc. The biometric application (identification or verification) has widely used and majority of research studies have conducted in the field of security system with different technologies (modalities or traits) such as fingerprint, iris, palmprint, face, retina etc[1]. The main objective of any biometric system is to achieve low cost, less error rate, speed in performance and high accuracy. There are two types of palmprint which called high resolution and low resolution. Each type is suitable for different applications. High resolution images are used for forensicapplication[3] while low resolution images are used for access controls application
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