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Face recognition method based on convolutional neural network

A convolutional neural network and face recognition technology, applied in neural learning methods, biological neural network models, neural architectures, etc., can solve problems such as complex calculations, unsatisfactory recognition results, and difficulty in meeting face recognition needs , to achieve a good recognition effect

Inactive Publication Date: 2018-08-21
UNIV OF SCI & TECH LIAONING
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AI Technical Summary

Problems solved by technology

The calculations of these face recognition methods are relatively complicated, and manual operations are required to complete them in actual use.
In addition, the above methods are difficult to meet the needs of face recognition with a large amount of data, so the recognition effect obtained is not satisfactory and needs to be further improved

Method used

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  • Face recognition method based on convolutional neural network
  • Face recognition method based on convolutional neural network
  • Face recognition method based on convolutional neural network

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Embodiment Construction

[0052] The specific embodiments provided by the present invention will be described in detail below in conjunction with the accompanying drawings.

[0053] figure 1 The block diagram of this system includes:

[0054] Step 1: Select the database of face images and preprocess the face images;

[0055] Step 2: Build a convolutional neural network model;

[0056] Step 3: training of convolutional neural network model zy_net;

[0057] Step 4: Output classification results.

[0058] figure 2 It is the network model structure of the present invention, which includes 5 layers of Mlp convolution layers, 4 layers of downsampling layers, two fully connected layers and a Softmax classification layer.

[0059] image 3 It is the preprocessed face image. The original image in the face data set has various forms, so in order to improve the recognition effect, it is necessary to preprocess the face image in the face database (face feature point detection, face alignment) , face clipping...

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Abstract

The invention provides a face recognition method based on a convolutional neural network. According to the face recognition method based on deep learning, shortcomings of traditional face recognitionmethods are overcome, and large-scale training is carried out on a CASIA-WebFace face data set through constructing a new convolutional neural network model. According to the network model of the invention, Mlp convolution layers are employed to improve feature extraction ability on faces, an MFM excitation function is used to increase nonlinearity on the model, and a Center Loss function is addedto improve classification ability of the network on the faces. Finally, the trained model is applied to face classification prediction and face verification, obtains a recognition rate of 90.3% in face classification prediction, and obtains an accuracy rate of 92.5% in face verification experiment.

Description

technical field [0001] The invention relates to the technical field of face recognition methods, in particular to a face recognition method based on a convolutional neural network. Background technique [0002] Since various countries have increased their supervision on national security and social security, and identification is one of the important means, biometric identification technology has gradually come into people's sight. Among them, face recognition technology provides a simple, easy and reliable way for human identity verification. Due to the rise of deep learning methods in recent years, the recognition effect of face recognition methods based on deep learning has improved a lot, and face recognition technology has made great progress. [0003] Face recognition is a major research hotspot in pattern recognition classification. The biggest difficulty in face recognition is how to distinguish between intra-class changes due to factors such as lighting, actions, ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06N3/04G06N3/08
CPCG06N3/08G06V40/161G06V40/172G06N3/045
Inventor 赵骥吴晓翎朱玉
Owner UNIV OF SCI & TECH LIAONING
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