Abstract
Vein recognition is a new identity authentication technology. It attracts many researchers' attention due to its good security and reliability. This paper proposes a wrist vein recognition system. The proposed system identifies people according to the characteristics of their wrist veins. A special camera is reformed to obtain the wrist vein images and an image dataset is established. Principal component analysis (PCA) is adopted to eliminate the redundant information in the images and extract their global features. The global features are classified by Two-hidden-layer Extreme Learning Machine (TELM). TELM is compared with original Extreme Learning Machine (ELM) and other two algorithms Support Vector Machine (SVM) and Naive Bayes (NB). Experiment results show that the accuracy of the proposed system is higher than the other three algorithms. Though the speed of TELM is not the fastest, it is able to recognize images within satisfactory time.
Highlights
Nowadays, identity authentication is needed on many occasions such as door access control system, registration system and online payment
This paper proposes a wrist vein recognition system due
The proposed system is compared with Extreme Learning Machine (ELM), Support Vector Machine (SVM) and Naive Bayes (NB)
Summary
Identity authentication is needed on many occasions such as door access control system, registration system and online payment. This paper proposes a wrist vein recognition system due Many different features are adopted in finger and hand vein recognition system. The principal component analysis is adopted to extract wrist vein global features.
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