Abstract
Palm-print recognition has been investigated over many decades. Palm-print recognition has many phases in which region mapping and feature extraction play an important role. In this paper, we propose a region mapping model using Gradient Bidirectional Boundary Tracing (GBBT) method. We also propose a novel scheme of palm-print recognition using skeleton morphological (SM) operations to extract the combined shape, texture and colour features of the gray-scale palm-print image. To this extent we explore the idea of GBBT and SM that comprises of 11 features whose responses on the given palm-print image are processed independently and classified using Multi-SVM (MSVM). The proposed model is compared with KNN and SVM to demonstrate its efficiency. Extensive experiments are carried out on different large-scale publicly available palm-print databases in terms of its sensitivity specificity and accuracy. The experimental results indicate that our approach produces 2.509% and 2.6342% more efficacy than SVM and KNN methods, respectively.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Biomedical Engineering and Technology
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
