A finger vein recognition method based on an FCN full convolution network

A fully convolutional network and recognition method technology, applied in character and pattern recognition, biological neural network models, instruments, etc., can solve the problem that accurate and robust features are difficult to extract, difficult to separate vein area and background area, and recognition success rate is dependent, etc. problems to achieve high precision and accuracy

Inactive Publication Date: 2019-05-28
ZHEJIANG SCI-TECH UNIV
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Problems solved by technology

[0004] At present, the problems to be solved are: the success rate of recognition depends on the imaging quality of finger vein images, it

Method used

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  • A finger vein recognition method based on an FCN full convolution network
  • A finger vein recognition method based on an FCN full convolution network
  • A finger vein recognition method based on an FCN full convolution network

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Embodiment Construction

[0049] The present invention will be described in detail below in conjunction with the accompanying drawings and specific implementation, but not as a limitation of the present invention.

[0050] Such as figure 1 , the implementation steps of this method are as follows:

[0051] A. Connect finger vein collection equipment, image collection

[0052] Use the data cable attached to the finger vein collection device to connect the client computer with the device, and install the driver program required by the device on the client computer. Place your finger in the corresponding position according to the requirements of the acquisition device, and wait for the device to collect finger vein images.

[0053] B. Reduce the noise of image data by preprocessing

[0054] In the previous step, we obtained the vein image of the finger through the finger vein acquisition device. Preliminary enhancement of vein images by contrast-limited adaptive histogram equalization. Divide the ima...

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Abstract

The invention discloses a finger vein recognition method based on an FCN full convolution network. The finger vein recognition method comprises the steps that collecting a finger vein image; preprocessing the finger vein image to obtain a preprocessed finger vein image; performing edge detection, least square linear fitting and direction correction on the preprocessed finger vein image, and extracting a median section ROI finger vein image with rickest finger veins on the basis of the edge detection, the least square linear fitting and the direction correction; extracting features from the extracted ROI finger vein image by adopting the trained FCN full convolution network, and classifying each pixel point to obtain a to-be-registered/recognized finger vein image with distinguished vein points and background points; and collecting a to-be-registered finger vein image, constructing a registered finger vein database, and carrying out retrieval and recognition on the to-be-recognized finger vein image in the finger vein database to obtain a matching recognition result. In the embodiment of the invention, a clearer finger vein image can be obtained, and the matching recognition efficiency is higher and more accurate.

Description

technical field [0001] The invention relates to the fields of biometric feature recognition technology, image recognition and deep learning, and in particular to a finger vein recognition method based on an FCN full convolution network. Background technique [0002] The research and application of identity authentication technology based on biometrics is becoming more and more extensive; the current society's demand for safer and more friendly identity authentication has put forward higher requirements for biometric technology; and finger veins are alive and unique. It will produce feature duplication and allow non-contact identification, so it has become a category that has attracted more attention in the field of biometrics. [0003] Most of the current finger vein recognition methods are based on the knowledge in the field, designing image processing, filtering, etc.; when the imaging quality of the acquisition equipment is low, it is difficult to artificially design feat...

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Application Information

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IPC IPC(8): G06K9/00G06K9/32G06K9/62G06N3/04
Inventor 包晓安王强张娜包剑平涂小妹易芮陈春宇
Owner ZHEJIANG SCI-TECH UNIV
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