Lightweight facial recognition method for edge computing

An edge computing and facial recognition technology, applied in the field of deep learning, can solve the problems of waste of resources, poor performance, etc., to achieve the effect of less calculation, high accuracy, and reduced complexity

Inactive Publication Date: 2020-02-28
SOUTH CHINA NORMAL UNIVERSITY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

If these networks are applied for object classification, this will result in waste of resources and poor performance

Method used

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  • Lightweight facial recognition method for edge computing
  • Lightweight facial recognition method for edge computing
  • Lightweight facial recognition method for edge computing

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Embodiment

[0039] In this embodiment, a lightweight face recognition method for edge computing is designed, and a lightweight convolutional neural network classification model for edge computing with small size input is designed, named AntCNN, as figure 1 Shown, method of the present invention comprises the following steps:

[0040] S1. Construct a lightweight facial recognition network model AntCNN oriented to edge computing devices. The network structure of AntCNN includes: a first convolutional layer, a first pooling layer, a first dense block, a second pooling layer, a second dense block, third pooling layer, third dense block, and third pooling layer;

[0041] S2, capturing the facial image, and compressing the facial image into small size pixels, as the input of AntCNN;

[0042] This embodiment first uses the dlib library to capture facial images, which captures a face at a random time from 0.6 to 3 seconds. Capturing faces from video is smooth and doesn't stutter. In this embod...

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Abstract

The invention discloses a lightweight language face identification method for edge computing. The method comprises the following steps: S1, constructing a lightweight facial recognition network modelAntCNN oriented to edge computing equipment, the network structure of the AntCNN comprising a first convolution layer, a first pooling layer, a first dense block, a second pooling layer, a second dense block, a third pooling layer, a third dense block and a third pooling layer; S2, capturing a face image, compressing the face image into small-size pixels to serve as input of an AntCNN, and utilizing the AntCNN to perform feature extraction and classification, S3, enabling the acquired multi-dimensional feature map to pass through a full connection layer to obtain specific scores of all classes, and enabling the maximum score to represent the specific classification of the picture. According to the method, a dlib library of traditional machine learning is used for searching a face part, themethod is successfully operated on the edge computing device of the Raspberry Pi, the video for searching the face is very smooth, and the real-time requirement is completely met.

Description

technical field [0001] The invention belongs to the technical field of deep learning, and in particular relates to a lightweight face recognition method oriented to edge computing. Background technique [0002] Deep learning has better robustness to the diversity changes of the target. Therefore, directly running deep learning network models on edge computing devices is regarded as the most promising method and has been widely researched and applied. However, deep learning is computationally intensive. However, the calculation amount and storage space of edge computing devices are limited. This means that while designing a deep learning network model for edge computing, it is necessary to consider the accuracy rate and also pay attention to the amount of calculations and parameters required by the network. In order to be able to run deep learning network models on edge computing devices, lightweight network models such as MobileNet and ShuffleNet have been proposed. Howe...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06N3/04G06N3/08
CPCG06N3/08G06V40/172G06N3/045
Inventor 龚征杨顺志叶开魏运根
Owner SOUTH CHINA NORMAL UNIVERSITY
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