Development of Feature Extraction Algorithm for Finger Vein Recognition


KIPS Transactions on Software and Data Engineering, Vol. 7, No. 9, pp. 345-350, Sep. 2018
10.3745/KTSDE.2018.7.9.345, Full Text:
Keywords: Finger Vein Recognition, feature extraction, Node Extraction, SWDA, pattern recognition
Abstract

This study is an algorithm for detecting vein pattern features important for finger vein recognition. The feature detection algorithm is important because it greatly affects recognition results in pattern recognition. The recognition rate is degraded because the reference is changed according to the finger position change. In addition, the image obtained by irradiating the finger with infrared light is difficult to separate the image background and the blood vessel pattern, and the detection time is increased because the image preprocessing process is performed. For this purpose, the presented algorithm can be performed without image preprocessing, and the detection time can be reduced. SWDA (Down Slope Trace Waveform) algorithm is applied to the finger vein images to detect the fingertip position and vein pattern. Because of the low infrared transmittance, relatively dark vein images can be detected with minimal detection error. In addition, the fingertip position can be used as a reference in the classification stage to compensate the decrease in the recognition rate. If we apply algorithms proposed to various recognition fields such as palm and wrist, it is expected that it will contribute to improvement of biometric feature detection accuracy and reduction of recognition performance time.


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Cite this article
[IEEE Style]
T. Kim and S. Lee, "Development of Feature Extraction Algorithm for Finger Vein Recognition," KIPS Transactions on Software and Data Engineering, vol. 7, no. 9, pp. 345-350, 2018. DOI: 10.3745/KTSDE.2018.7.9.345.

[ACM Style]
Taehoon Kim and Sangjoon Lee. 2018. Development of Feature Extraction Algorithm for Finger Vein Recognition. KIPS Transactions on Software and Data Engineering, 7, 9, (2018), 345-350. DOI: 10.3745/KTSDE.2018.7.9.345.