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Human ear recognition method based on fusion of gradient direction histogram and local binary pattern

A local binary pattern, gradient direction technology, applied in character and pattern recognition, computer parts, instruments, etc., can solve problems such as two-dimensional image deformation, and achieve high accuracy and low computational complexity to overcome noise interference Effect

Active Publication Date: 2019-08-30
NANJING UNIV OF POSTS & TELECOMM
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AI Technical Summary

Problems solved by technology

However, when there is a change in the rotation angle of the human ear, its two-dimensional image will cause a large deformation. At this time, the recognition rate of the traditional method will drop sharply. Therefore, a more cost-effective and accurate human ear recognition method still needs to be developed a lot of research work

Method used

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  • Human ear recognition method based on fusion of gradient direction histogram and local binary pattern
  • Human ear recognition method based on fusion of gradient direction histogram and local binary pattern
  • Human ear recognition method based on fusion of gradient direction histogram and local binary pattern

Examples

Experimental program
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Embodiment Construction

[0043] In specific implementation, figure 1 It is a human behavior recognition method flow based on convolutional neural network.

[0044] This example uses the human ear experiment database of Beijing University of Science and Technology as the experimental object, including 77 human ear images. There are four human ear images for each human ear in the human ear library, which are: the frontal image of the human ear under normal conditions, the +30 degree and -30 degree rotated images of the human ear, and the frontal image of the human ear under light and dark conditions .

[0045] In the specific implementation, each person has 4 ear images, of which 3 images are used for training and 1 image is used for testing.

[0046] First, input 3 ear images of each person into the system for color standardization processing, use H(x, y)=0.3* R(x, y)+0.59*G(x, y)+0.11*B( x, y) converts the image into a grayscale image, where R(x, y), G(x, y), and B(x, y) are the red, green, and blue ...

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Abstract

The invention discloses a human ear recognition method based on fusion of a gradient direction histogram and a local binary pattern, which solves the problem of low human ear recognition rate in a human ear image. According to the method, firstly, the gradient direction histogram of the image is extracted, dimensionality reduction is carried out through a principal component analysis method, thenthe texture features of the image are extracted through the local binary pattern, then the two features are fused, and finally classification is carried out through a minimum distance classifier. According to the invention, through the multi-feature fusion, the recognition rate of human ear recognition is improved, and the method has good implementability and effectiveness.

Description

technical field [0001] The invention relates to a human ear recognition method based on the fusion of a gradient direction histogram and a local binary pattern, and belongs to the interdisciplinary technical fields of biological feature recognition, deep learning, and artificial intelligence. Background technique [0002] As a new biometric identification technology, human ear recognition has attracted more attention from scholars at home and abroad in the past two years, and its theory and application research has important theoretical significance and practical application value. [0003] Human ear recognition uses human ear images as the research object for feature recognition, which can be used as a useful supplement to other biometric technologies, or it can be used alone in the occasion of individual identification. In the biometric-based identification technology, human ear recognition has many advantages, such as the small size of the human ear image, the small amoun...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/46G06K9/62
CPCG06V40/171G06V40/172G06V10/44G06V10/507G06V10/467G06F18/2135G06F18/24
Inventor 赵立昌陈志岳文静吴宇晨孙斗南
Owner NANJING UNIV OF POSTS & TELECOMM
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