Authors: G. Yashodha; R. Bremananth
Addresses: Department of Computer Applications, Sri Ramakrishna Engineering College, Coimbatore, India ' Information Systems & Technology Department, Sur University College, Sur, Oman
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.
Keywords: region mapping; GBBT; skeleton morphological; colour; shape; texture; feature extraction; SVM; MSVM; KNN.
International Journal of Biomedical Engineering and Technology, 2017 Vol.25 No.2/3/4, pp.227 - 249
Available online: 23 Oct 2017 *Full-text access for editors Access for subscribers Purchase this article Comment on this article