东北大学学报（自然科学版）

东北大学学报:自然科学版 ›› 2019, Vol. 40 ›› Issue (10): 1376-1381.DOI: 10.12068/j.issn.1005-3026.2019.10.002

• 信息与控制 • 上一篇    下一篇

基于加权稀疏非负矩阵分解的车脸识别算法

石春鹤1， 吴成东2   

  1. (1. 东北大学 信息科学与工程学院， 辽宁 沈阳110819; 2. 东北大学 机器人科学与工程学院， 辽宁 沈阳110819)
  • 收稿日期:2018-12-04 修回日期:2018-12-04 出版日期:2019-10-15 发布日期:2019-10-10
  • 通讯作者: 石春鹤
  • 作者简介:石春鹤(1975-)，男，辽宁灯塔人，东北大学博士研究生; 吴成东(1960-)，男，辽宁大连人，东北大学教授，博士生导师.
  • 基金资助:
    国家自然科学基金资助项目(61503274，61701101)； 国家重点机器人工程项目 (2017YFB1300900，2017YFB1301103)； 沈阳市科技计划项目(17-87-0-00，18-013-0-15).

Vehicle Face Recognition Algorithm Based on Weighted and Sparse Nonnegative Matrix Factorization

SHI Chun-he1， WU Cheng-dong2   

  1. 1. School of Information Science & Engineering， Northeastern University， Shenyang 110819， China; 2. School of Robot Science & Engineering， Northeastern University， Shenyang 110819， China.
  • Received:2018-12-04 Revised:2018-12-04 Online:2019-10-15 Published:2019-10-10
  • Contact: SHI Chun-he
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摘要： 为提高多种光照条件下交通卡口视频中车脸识别的准确性，提出了一种基于改进非负矩阵分解的车脸识别算法.对采集图像进行预处理，获得车脸图像与车牌信息.基于特定光照条件，自适应提取车脸图像的初始特征.针对车脸图像中像素位置的重要性差异，建立了加权稀疏约束非负矩阵分解的特征降维方法.通过判断特征相似性与车牌信息一致性，确定车辆是否合法.实验结果表明所提算法具有较好的识别性能，真实接受率与错误拒绝率分别可达到0.9875与0.04，并满足实时性要求.

关键词: 车脸识别, 视频处理, 车牌识别, 非负矩阵分解, 稀疏表示

Abstract: In order to improve the vehicle face recognition accuracy in traffic videos under various illumination conditions， a vehicle face recognition algorithm based on improved nonnegative matrix factorization(NMF)was proposed. The vehicle face image and license plate information were acquired after image preprocessing. The original feature of vehicle face image was extracted adaptively based on the special illumination condition. For the importance variation of different pixels in vehicle face image， a feature dimension reduction based on weighted and sparse NMF(WSNMF)was established. The vehicle legality can be defined by determining the similarity of features and the consistency of license plates. The experimental results showed that the proposed algorithm has better recognition performance， and genuine acceptance rate(GAR)and false rejection rate(FRR)can reach 0.9875 and 0.04， respectively， and meet the real-time requirements.

Key words: vehicle face recognition, video processing, license plate recognition, nonnegative matrix factorization, sparse representation

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