Al technical title is built by PatSnap Al team. It summarizes the technical point description of the patent document.
A convolutional neural network and recognition method technology, applied in the field of embedded finger vein recognition, can solve the problems of low image quality, attack and deceive the recognition system, and reduce user experience, and achieve the effect of improving recognition accuracy and security.
Active Publication Date: 2017-10-24
GUANGZHOU GUANGDA INNOVATION TECH CO LTD
View PDF13 Cites 22 Cited by
Summary
Abstract
Description
Claims
Application Information
AI Technical Summary
This helps you quickly interpret patents by identifying the three key elements:
Problems solved by technology
Method used
Benefits of technology
Problems solved by technology
Although its current development trend is good, due to the problem of its own imaging mechanism, the image quality collected by the existing finger vein recognition system is not high. The main reasons include: the design problem of the collection device, and the different thickness of different fingers is not fully considered. The situation; the image deviation caused by the axial deflection of the finger is not considered, so the current system basically
Method used
the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more
Image
Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
Click on the blue label to locate the original text in one second.
Reading with bidirectional positioning of images and text.
Smart Image
Examples
Experimental program
Comparison scheme
Effect test
Embodiment 1
[0043] This embodiment discloses an embedded finger vein recognition method based on a convolutional neural network with counterfeit detection capability, as shown in the attached file. figure 1 As shown, specifically including the following steps:
[0044] S1. Collect several finger vein images with multi-level light intensity, select the finger vein image with the highest definition, and then preprocess the image ROI area;
[0045] S2. Use the local binary pattern LBP to encode the texture feature of the high-frequency information of the finger vein image;
[0046] S3. Extract the high-frequency part features of the finger vein image through the high-pass filter, and extract the finger image features through the vein recognition shallow convolutional neural network;
[0047] S4. Use the SVM classifier to perform counterfeit detection to distinguish true and false vein images.
[0048] Among them, in step S1, a Butterworth high-pass filter is used to obtain high-frequency images of fin...
Embodiment 2
[0077] This embodiment discloses an embedded finger vein recognition method based on a convolutional neural network and capable of counterfeiting detection, which specifically includes the following steps:
[0078] S1. Collect several finger vein images with multi-level light intensity, select the finger vein image with the highest definition, and then preprocess the image ROI area;
[0079] S2. Use the local binary pattern LBP to encode the texture feature of the high-frequency information of the finger vein image;
[0080] S3. Extract the high-frequency part features of the finger vein image through the high-pass filter, and extract the finger image features through the vein recognition shallow convolutional neural network;
[0081] S4. Use the SVM classifier to perform counterfeit detection to distinguish true and false vein images.
the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more
PUM
Login to view more
Abstract
The present invention discloses a convolutional neural network-based embedded finger vein identification method enabling a counterfeit detection capability. The method includes the following steps that: S1, a plurality of finger vein images of a plurality of levels of light intensities are acquired, a finger vein image with the highest resolution is selected, the ROI (region of interest) of the captured image is preprocessed; S2, a local binary pattern (LBP) is utilized to perform texture feature coding on the high frequency information of the finger vein image; S3, the high frequency part features of the finger vein image are extracted through using a high-pass filter, finger image features are extracted through a vein identification shallow convolutional neural network; and S4, an SVM (support vector machine) classifier is utilized to perform counterfeit detection so as to distinguish the authenticity of the vein image. With the method adopted, the problem that a printed counterfeited vein image can fool an existing identification system can be solved, and the security of an actual vein identification system can be improved; the problem of the decrease of the identification accuracy of the actual system which is caused by factors such as low vein image quality and finger axial deflection can be solved.
Description
Technical field [0001] The invention relates to the technical fields of biological feature recognition, image processing and pattern recognition, and deep learning, in particular to an embedded finger vein recognition method based on a convolutional neural network and having a counterfeit detection capability. Background technique [0002] Biometric recognition technology refers to the use of unique physiological or behavioral characteristics of a person, such as face, fingerprint, iris, palmprint, voiceprint and signature, etc., through some pattern recognition algorithms for identity recognition. Compared with traditional identification methods, biometric identification technology is more reliable, safe and convenient. Finger vein recognition technology, as an emerging biometric identification technology, has gained a place in the field of biometric identification with its unique live detection capabilities. Compared with other biometric identification, finger vein recognition ...
Claims
the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more
Application Information
Patent Timeline
Application Date:The date an application was filed.
Publication Date:The date a patent or application was officially published.
First Publication Date:The earliest publication date of a patent with the same application number.
Issue Date:Publication date of the patent grant document.
PCT Entry Date:The Entry date of PCT National Phase.
Estimated Expiry Date:The statutory expiry date of a patent right according to the Patent Law, and it is the longest term of protection that the patent right can achieve without the termination of the patent right due to other reasons(Term extension factor has been taken into account ).
Invalid Date:Actual expiry date is based on effective date or publication date of legal transaction data of invalid patent.
Login to view more 
Login to view more