Palm Vein Recognition System Based on Derived Pattern and Feature Vectors

Aderonke Lawal, Segun Aina, Samuel Okegbile, Seun Ayeni, Dare Omole and Adeniran Ishola Oluwaranti
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Aderonke Lawal: Department of Computer Science and Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria
Segun Aina: Department of Computer Science and Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria
Samuel Okegbile: Department of Computer Science and Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria
Seun Ayeni: Obafemi Awolowo University, Ile-Ife, Nigeria
Dare Omole: Obafemi Awolowo University, Ile-Ife, Nigeria
Adeniran Ishola Oluwaranti: Obafemi Awolowo University, Ile-Ife, Nigeria

International Journal of Digital Literacy and Digital Competence (IJDLDC), 2017, vol. 8, issue 2, 56-72

Abstract: Biometrics is a technology for recognition under which Palm vein recognition stems. They are of crucial importance in various applications of high sensitivity. This article develops a palm vein recognition model, based on derived pattern and feature vectors. All the palm print images used in this work were obtained from CASIA Multi-Spectral Palmprint Image Database V1.0 (CASIA database). First, a Region of Interest (ROI) was identified and extracted from the palm print images. Next, Histogram Equalization was used to enhance the area of the palm print image in the Region of Interest. The enhanced image obtained was subjected to the Zhang Suen's Thinning Algorithm to extract appropriate features in the palm print images needed for authentication. The features derived based on this vascular pattern thinning algorithm which are then compared and evaluated to carry out ‘matching'. The Pattern Matching itself was done using the Euclidean Distance for subsequent matching. The model was designed using UML, and implemented with C# and MS SQL on Microsoft Visual Studio platform. The developed system was evaluated based on False Acceptance, False Rejection and Equal Error Rate (EER) values obtained from the system. The results of testing and evaluation show that the developed system has achieved high recognition accuracy.

Date: 2017
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