一种基于LVQ神经网络模型的人脸检测方法

王艳; 祁萌

doi:10.37188/CJLCD.2020-0295

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一种基于LVQ神经网络模型的人脸检测方法

图像处理 | 更新时间：2021-07-13

- 一种基于LVQ神经网络模型的人脸检测方法
- Face detection algorithm based on LVQ neural network model
- 液晶与显示 2021年36卷第7期页码：1027-1034
- 作者机构：
  
  成都工业学院电子工程学院, 四川成都 610031
- 作者简介：
  
  [ "王艳(1986-), 女, 重庆万州人, 硕士, 讲师, 2011年于重庆大学获得硕士学位, 主要研究方向为数字图像处理、计算机视觉。E-mail:1423950761@qq.com" ]
  [ "祁萌(1989-), 女, 河南平顶山人, 硕士, 助理研究员, 2016年于四川大学获得硕士学位, 主要从事智能算法的研究。E-mail: 983629548@qq.com" ]
- 基金信息：
  
  国家自然科学基金(61705022);四川省科技计划项目(2019YJ0377);四川性社会学与性教育研究中心课题(SXJYZ2009)
- DOI：10.37188/CJLCD.2020-0295
  中图分类号： TP394.1
- 收稿日期：2020-11-03，
  
  修回日期：2020-11-24，
  
  纸质出版日期：2021-07
- 稿件说明：
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王艳, 祁萌. 一种基于LVQ神经网络模型的人脸检测方法[J]. 液晶与显示, 2021,36(7):1027-1034.

Yan WANG, Meng QI. Face detection algorithm based on LVQ neural network model[J]. Chinese journal of liquid crystals and displays, 2021, 36(7): 1027-1034.
王艳, 祁萌. 一种基于LVQ神经网络模型的人脸检测方法[J]. 液晶与显示, 2021,36(7):1027-1034. DOI： 10.37188/CJLCD.2020-0295.

Yan WANG, Meng QI. Face detection algorithm based on LVQ neural network model[J]. Chinese journal of liquid crystals and displays, 2021, 36(7): 1027-1034. DOI： 10.37188/CJLCD.2020-0295.

摘要

人脸检测是人脸识别的首要环节。为快速准确地从复杂背景中定位出人脸区域，本文提出一种基于LVQ神经网络的人脸检测算法。通过分析肤色特征，将图像分别转换到YCbCr空间和HSI空间，提取

、

颜色分量用来描述人脸图像特征。构建结构为4-20-2的LVQ神经网络模型，选取100幅图像作为LVQ神经网络的训练样本，20幅图像作为测试样本，迭代次数为150次，误差为0.001。经过训练，得到了有效的权值。利用训练后的神经网络，分别在LFW、AFW和Faces数据集上进行了性能测试，在3个数据集的正检率分别为76.82%，84.42%，100%，误检率分别为17.34%，12.34%，0，漏检率分别为21.55%，15.63%，0。实验结果表明，相对于传统的基于肤色特征的人脸检测方法，本方法在正检率、误检率及漏检率方面均表现出良好的优越性。

Abstract

Face detection is the first step of face recognition. In order to fix the face region from the complex background rapidly

a method based on LVQ is studied in this paper. By analyzing the color features of skin

images are converted into YCbCr space and HSI space firstly

and then

color components are extracted to describe the facial image features. An LVQ neural network with a structure of 4-20-2 is constructed

100 images as training samples and 20 images as test samples are selected for this neural network. The number of iterations is 150 and the error is 0.001. After training

the effective weight are obtained. Using the trained neural network

the performance tests are performed on the LFW

Faces

and AFW datasets. The positive detection rates in the three data sets are 76.82%

84.42%

100%

the false detection rates are 17.34%

12.34%

and 0

and the missed detection rates are 21.55%

15.63%

and 0

respectively. The experimental results show that the method in our paper shows good superiority in terms of positive detection rate

false detection rate and missed detection rate compared with the traditional face detection method based on skin color features.

关键词

Keywords

references

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