基于迹变换和旋转增量调制特征的模糊人脸识别

汪宇玲; 陈立; 黎明; 钟国韵; 何月顺; 常玉祥; 宋伟宁

doi:10.12263/DZXB.20200599

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基于迹变换和旋转增量调制特征的模糊人脸识别

学术论文 | 更新时间：2025-12-08

- 基于迹变换和旋转增量调制特征的模糊人脸识别
- Rotational Delta Modulation Feature and Its Application in Blurry Face Recognition Based on Trace Transform
- 电子学报 2021年49卷第12期页码：2437-2448
- 作者机构：
  
  1.东华理工大学江西省核地学数据科学与系统工程技术研究中心, 江西南昌 330013
  2.南昌航空大学江西省图像处理与模式识别重点实验室，江西南昌 330063
- 作者简介：
  
  [ "汪宇玲女，1976年生于江西抚州.现为东华理工大学信息工程学院副教授，硕士生导师，主要研究方向为模式识别、计算机视觉.E‑mail:ylwang@ecut.edu.cn" ]
  [ "陈立男，1995年出生于河南开封.现为东华理工大学信息工程学院研究生，主要研究方向为图像处理与模式识别.E‑mail:873499635@qq.com" ]
  [ "黎明（通信作者）男，1965年生于江西樟树.现为南昌航空大学信息工程学院教授，博士生导师，主要研究方向为模式识别与智能计算.E‑mail:liming@nchu.edu.com" ]
  [ "钟国韵男，1979年生于浙江浦江，现为东华理工大学信息工程学院副教授，硕士生导师，主要研究方向为图像处理、视频处理和编码. E‑mail:gyzhong55@163.com" ]
- 基金信息：
  
  国家自然科学基金(62066003;61866025;D0213);国家重点研发计划(2018YFB1702702);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(JETRCNGDSS202006)
- DOI：10.12263/DZXB.20200599
  中图分类号： TP391.4;TN911.73
- 收稿：2020-06-22，
  
  修回：2021-08-03，
  
  纸质出版：2021-12-25
- 稿件说明：
移动端阅览
汪宇玲,陈立,黎明等.基于迹变换和旋转增量调制特征的模糊人脸识别[J].电子学报,2021,49(12):2437-2448.

WANG Yu-ling,CHEN Li,LI Ming,et al.Rotational Delta Modulation Feature and Its Application in Blurry Face Recognition Based on Trace Transform[J].ACTA ELECTRONICA SINICA,2021,49(12):2437-2448.
汪宇玲,陈立,黎明等.基于迹变换和旋转增量调制特征的模糊人脸识别[J].电子学报,2021,49(12):2437-2448. DOI： 10.12263/DZXB.20200599.

WANG Yu-ling,CHEN Li,LI Ming,et al.Rotational Delta Modulation Feature and Its Application in Blurry Face Recognition Based on Trace Transform[J].ACTA ELECTRONICA SINICA,2021,49(12):2437-2448. DOI： 10.12263/DZXB.20200599.

摘要

为提高各种不同特殊场景下的模糊人脸识别精确性和鲁棒性，本文提出一种基于迹变换和旋转增量调制编码的特征提取方法.该方法将通信语音编码技术与图像变换技术相结合，首先通过迹线旋转扫描整幅图像，并对迹线上的采样信息进行增量调制编码，从而获得多角度的全局有序结构特征，然后用支持向量机对样本图像的这些特征进行训练以分类并识别图像.实验结果表明，在各种不同模糊级的低质量人脸数据库上，本文方法对不同光照变化、不同拍摄角度、不同遮挡等不同场景的人脸图像均能取得较好的识别效果，与一些传统方法相比识别性能大幅提升，相对于VGGNet和Sphereface两种先进方法在三组不同模糊度测试图像集的平均识别率分别提高2.18%和2.20%，具有更高的识别精度和更好的鲁棒性.

Abstract

For the accuracy and robustness of the blurry face image recognition in various special scenarios; this paper proposes a novel rotation delta modulation texture extraction method based on trace transform and delta modulation encoding. The method combines the communication speech coding with the image transformation technology. Firstly

the whole image is scanned by the rotating trace line based on trace transformation theory. Secondly

the sampled pixels on the trace line are encoded used delta modulation technology. The global ordered structure features are obtained in various angles. Finally

the features are trained by support vector machine for blurry face image classification and recognition. The experimental results indicate the proposed method has the better performance of blurry face image recognition under illumination fluctuations

various camera angles and image occlusions

and so on

on a variety of low-quality face image sets with different blur levels. The average recognition ratio are increased 2.18% and 2.20% compared to VGG and Sphereface methods on the face image sets with three blur levels

which indicates the proposed method has the higher accuracy and better robustness.

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references

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