• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

计算机工程与科学

• 图形与图像 • 上一篇    下一篇

旋转均值跳动特征及其在模糊人脸识别中的应用

钟国韵,王檬檬,汪宇玲,常艳荣,吴忠粱   

  1. (东华理工大学江西省放射性地学大数据技术工程实验室,江西 南昌 330013)
  • 收稿日期:2019-06-28 修回日期:2019-09-24 出版日期:2020-03-25 发布日期:2020-03-25
  • 基金资助:
    国家自然科学基金(61402102);江西省自然科学基金(20171BAB202005);江西省教育厅科技项目(GJJ170443,GJJ170432);江西省图像处理与模式识别重点实验室开放基金(ET201880042);江西省核地学数据科学与系统工程技术研究中心开放基金(JETRCNGDSS201802);江西省放射性地学大数据技术工程实验室开放基金(JELRGBD201701,JELRGBDT201804)
     

A rotation mean pulsation feature extraction method
and its application in fuzzy face recognition

ZHONG Guo-yun,WANG Meng-meng,WANG Yu-ling,CHANG Yan-rong,WU Zhong-liang   

  1. (Jiangxi Radioactive Geoscience Large Data Technology Engineering Laboratory,
    East China University of Technology,Nanchang 330013,China)
  • Received:2019-06-28 Revised:2019-09-24 Online:2020-03-25 Published:2020-03-25

摘要:

针对目前监控摄像头由于远距离拍摄导致模糊人脸识别率欠佳的问题,提出了具有“有序”全局结构性特征的旋转均值跳动特征提取算法。该算法在图像每条垂线上按照从上至下的顺序等分选择若干采样点,运用均值跳动的方法进行编码,计算每条垂线上所有值不为0的像素的平均值,按顺序将选取的若干采样点像素值和平均值进行比较,并依次编码,生成1个8位二进制数,其对应十进制值的范围与像素值范围相同,该十进制数为整条垂线上的特征值,从而提取出描述每条垂线的纹理特征信息。结合图像预处理和直方图归一化实现对纹理图像融合特征信息提取。实验结果表明,该算法相比深度学习在模糊人脸识别方面有了明显提升。
 

关键词: 人脸识别, 特征提取, 均值跳动, 结构性特征, 全局特征

Abstract:

Aiming at the problem of poor recognition rate of blurred face caused by long-distance shooting of surveillance cameras at present, a method for extracting the pulsation feature of rotational mean with orderly global structural characteristics is proposed. In this method, several sampling points are selected equally on each vertical line of the image according to the order from top to bottom, and the mean pulsation method is used for coding. Firstly, the average values of all non-zero pixels on each vertical line are calculated, and the pixel values and average values of selected sampling points are compared and coded in turn in order to generate an 8-bit binary number. The range of the decimal values is the same as the range of the pixel values. The decimal number is the characteristic value of the whole vertical line, so the texture feature information describing each vertical line is extracted. Texture image fusion feature information is extracted by combining image preprocessing with histogram normalization. The experimental results show that the proposed method obviously outperforms deep learning in the field of fuzzy face recognition.
 

Key words: face recognition, feature extraction, mean pulsation, structural characteristics, global characteristics