A Fusion of Palm Print and Palm Vein Features for Person Identification Using Grey Coding Technique

2020 ◽  
Author(s):  
Mr. Namdeo D. Kapale ◽  
Dr. Kyatanavar D. N.
Keyword(s):  
Person Identification ◽  
Palm Print ◽  
Palm Vein

Palm Print and Palm Vein Joint Recognition System Based Video

2018 ◽  
Vol 38 (2) ◽  
pp. 0215004
Author(s):  
王浩 Wang Hao ◽  
康文雄 Kang Wenxiong ◽  
陈晓鹏 Chen Xiaopeng
Keyword(s):  
Recognition System ◽  
Palm Print ◽  
Palm Vein

Contactless Authentication Device using Palm Vein and Palm Print Fusion Biometric Technology for Post Covid World

2021 ◽  
Author(s):  
Kala H S ◽  
Shreya Kumar ◽  
Rakshanda B Reddy ◽  
Nishanth Shastry ◽  
Rounak Thakur
Keyword(s):  
Biometric Technology ◽  
Palm Print ◽  
Palm Vein

Palm Vein Recognition System Based on Derived Pattern and Feature Vectors

2017 ◽  
Vol 8 (2) ◽  
pp. 56-72 ◽  
Author(s):  
Aderonke Lawal ◽  
Segun Aina ◽  
Samuel Okegbile ◽  
Seun Ayeni ◽  
Dare Omole ◽  
...  
Keyword(s):  
Region Of Interest ◽  
High Sensitivity ◽  
Histogram Equalization ◽  
Recognition System ◽  
Thinning Algorithm ◽  
Feature Vectors ◽  
Palm Print ◽  
Vein Recognition ◽  
High Recognition Accuracy ◽  
Palm Vein

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.

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