Deep learning face identification method based on multiple-characteristic fusion

A multi-feature fusion and deep learning technology, which is applied in the field of deep learning face recognition based on multi-feature fusion, can solve the problems of reduced accuracy, high time consumption, and low recognition accuracy

Inactive Publication Date: 2016-08-31
HUBEI UNIV OF TECH
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AI Technical Summary

Problems solved by technology

The main problem with this method is that if the data set is large enough, it will take a lot of time to match and the accuracy will be reduced.
Overemphasizing the large distance between classes and

Method used

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  • Deep learning face identification method based on multiple-characteristic fusion
  • Deep learning face identification method based on multiple-characteristic fusion
  • Deep learning face identification method based on multiple-characteristic fusion

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Embodiment Construction

[0095] In order to facilitate those of ordinary skill in the art to understand and implement the present invention, the present invention will be described in further detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the implementation examples described here are only used to illustrate and explain the present invention, and are not intended to limit this invention.

[0096] please see figure 1 , a kind of deep learning face recognition method based on multi-feature fusion provided by the invention, comprises the following steps:

[0097] Step 1: Initialize weight decay parameter λ=3e-3, weight sparse penalty parameter β=3, randomly initialize weight parameter θ, initialize sparse coefficient p=0.3, hidden layer L1=200, hidden layer L2=200 and classification number k= 40.

[0098] Step 2: image feature extraction, including carrying out Gabor feature extraction to the original ORL face database and carrying out LBP featu...

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Abstract

The invention discloses a deep learning face identification method based on multiple-characteristic fusion. The method comprises the steps of firstly performing 2D gabor transforming on a to-be-tested orl face database and extracting the face database with a gabor characteristic; because of overlarge dimension of the picture being 92*112, reducing the dimension of the image to 32*32 according to a bilinear interpolation method; then fusing the original orl face database with the face database with the gabor characteristic; and finally coding in a stack type self-coding manner in deep learning, and calculating a weight parameter in a sofmax regression manner, and predicting an identification accuracy. According to the deep learning face identification method, after multiple characteristics are fused under a precondition that the testing accuracy of the characteristic through singly utilizing the algorithm is not lower than 80%, higher accuracy and high algorithm stability are realized; and even after random initialization, identification accuracy is basically unchanged.

Description

technical field [0001] The invention belongs to the technical field of image recognition and deep learning, and relates to a learning face recognition method, in particular to a deep learning face recognition method based on multi-feature fusion. Background technique [0002] Face recognition is a biometric technology for identity authentication based on human facial feature information. Capture images or video streams containing human faces through cameras or cameras, and automatically detect and track human faces in the images, and then match and recognize detected faces. [0003] Face recognition has a wide range of applications, especially plays a very important role in many fields such as security and anti-terrorism, financial payment, access control attendance, identity recognition, etc. It involves domain knowledge such as biomedicine, pattern recognition, image processing, machine study etc. [0004] Face recognition algorithms mainly include: [0005] ①Template m...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62
CPCG06V40/172G06V40/161G06V40/168G06F18/214
Inventor 熊炜刘哲向梦吴俊驰刘小镜徐晶晶赵诗云
Owner HUBEI UNIV OF TECH
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