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Deep learning-based small-area fingerprint comparison method

A deep learning and small-area technology, which is applied in the field of comparison of small-area fingerprints, can solve the problem that the performance of small-area fingerprint comparison cannot meet the needs of actual use.

Active Publication Date: 2017-11-24
HANGZHOU JINGLIANWEN TECH
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Problems solved by technology

[0004] In order to overcome the fact that the performance of the existing fingerprint comparison method for small-area fingerprint comparison cannot meet the actual use requirements, the present invention proposes a small-area fingerprint comparison based on deep learning that is effective for small-area fingerprint comparison and has good reliability. method

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Embodiment Construction

[0041] The present invention will be further described below in conjunction with the accompanying drawings.

[0042] refer to Figure 1 ~ Figure 4 , a small-area fingerprint comparison method based on deep learning, comprising the following steps:

[0043] 1) Extraction of relevant information about detail feature points of small-area fingerprint images: use traditional algorithms to find the position, direction, quality and other information of detail feature points (endpoints, bifurcation points) in small-area fingerprints;

[0044] 2) ROI interception: according to the detailed feature point position and direction information obtained in step 1), with the feature point as the center, the image is rotated and normalized according to the direction of the feature point, and a small block B with a size of 64×64 is intercepted;

[0045] 3) Convolutional network training: The network model of the convolutional neural network adopts a deep residual network, and uses the Caffe fra...

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Abstract

The invention discloses a deep learning-based small-area fingerprint comparison method. The method comprises the following steps of 1) finding position and direction information of a feature point in a small-area fingerprint; 2) according to the obtained position and direction information of the detail feature point in the step 1), performing rotation normalization on an image by taking the feature point as a center and the direction of the feature point as an X axis, and capturing a small block B of a set size; 3) performing convolutional network training: adopting a deep residual error network for a network model of a convolutional neural network, and training a training sample by using a Caffe framework; 4) performing semantic feature extraction; 5) performing fingerprint registration: enabling a user to perform registration in combination with a corresponding instruction in a registration process, wherein a registration template is composed of a feature point union set of a registration fingerprint image; and 6) performing fingerprint comparison, wherein a comparison score is determined by a mean value of a plurality of maximum values of similarity in to-be-matched images and the registration template. The deep learning-based small-area fingerprint comparison method provided by the invention is effectively suitable for small-area fingerprint comparison and is high in reliability.

Description

technical field [0001] The invention relates to technical fields such as neural network, image processing, pattern recognition, and fingerprint comparison, in particular to a comparison method for small-area fingerprints, which is suitable for identity authentication of smart mobile devices, access control systems, notebooks and other devices. Background technique [0002] With the advancement of science and technology, the traditional identity verification methods relying on citizen ID cards, work permits, and personal passwords have been unable to meet people's increasing needs for security and convenience due to their own shortcomings and deficiencies. And born. Biometric technology belongs to the category of pattern recognition that utilizes the inherent biological and behavioral characteristics of the human body for identity authentication. With the continuous improvement of image processing and pattern recognition technology, fingerprint recognition technology is wide...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/32G06K9/62G06N3/08
CPCG06N3/084G06V40/1347G06V40/1365G06V10/25G06F18/214
Inventor 张永良周冰祝江威姜晓丽
Owner HANGZHOU JINGLIANWEN TECH
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