Improved loss function strategy for multi source face image retrieval

Guo-yin REN; Xiao-qi LYU; Yu-hao LI

doi:10.37188/CJLCD.2021-0003

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Improved loss function strategy for multi source face image retrieval

Target Identification and Imaging | 更新时间：2021-11-08

- Improved loss function strategy for multi source face image retrieval
- Chinese Journal of Liquid Crystals and Displays Vol. 36, Issue 11, Pages: 1583-1596(2021)
- 作者机构：
  
  1.内蒙古科技大学机械工程学院, 内蒙古包头 014010
  2.内蒙古科技大学信息工程学院, 内蒙古包头 014010
  3.内蒙古工业大学, 内蒙古呼和浩特 010051
- 作者简介：
- 基金信息：
  
  Baotou Youth Innovative Talent Project(0701011904)
- DOI：10.37188/CJLCD.2021-0003
  CLC： TP391.4
- Received：05 January 2021，
  
  Revised：09 April 2021，
  
  Published：2021-11
- 稿件说明：
移动端阅览
Guo-yin REN, Xiao-qi LYU, Yu-hao LI. Improved loss function strategy for multi source face image retrieval[J]. Chinese journal of liquid crystals and displays, 2021, 36(11): 1583-1596.
DOI：

Guo-yin REN, Xiao-qi LYU, Yu-hao LI. Improved loss function strategy for multi source face image retrieval[J]. Chinese journal of liquid crystals and displays, 2021, 36(11): 1583-1596. DOI： 10.37188/CJLCD.2021-0003.

摘要

目前，公安机关刑侦部门大量的离线视频存储在监控网络的服务器中，为了在这些海量视频帧中提取嫌疑人目标人脸，设计了人脸检索系统。通过改变CNN网络的RELU结构，训练新的四分支孪生网络来获取深度特征，构造了一个新的四分支孪生网络。结合网上发布的逃犯人脸图片发起通缉，借助深度特征对比展开基于内容的图像检索（CBIR）。新的四分支孪生网络比熟悉的网络，如Alexnet、Googlenet、VGGNet和ResNet等收敛得更快，系统鲁棒性好。网络的共享权重设计使得检索具有较高的模型训练精度和检索精度。图像深度特征可以在摄像机之间快速在线共享。实验结果表明，该方法的平均检索精度（ARP）为98.74%，模型训练精度为99.51%，帧率为28 FPS。

Abstract

At present

a large number of offline videos are stored in the servers of the surveillance network in criminal investigation departments of public security organs

and the face retrieval system is designed. The new Quadruplet Network converges faster than the familiar networks such as Alexnet

Googlenet

VGGNet and ResNet. Because of the shared weight design of the network

the retrieval has a high precision

Average Retrieval Precision(ARP) and model training accuracy

and the system has good robustness. The image depth features can be shared quickly online between the cameras. The proposed method is effective

with ARP of 98.74% and a model training accuracy of 99.51%

and a frame rate of 28 FPS.

关键词

Keywords

references

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Guo-yin REN

Xiao-qi LYU

Yu-hao LI

Related Institution

School of Mechanical Engineering， Inner Mongolia University of Science & Technology

School of Information Engineering， Inner Mongolia University of Science & Technology

Inner Mongolia University of Technology Hohhot

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