人脸识别中基于学习的核图像微分滤波器

doi:10.11772/j.issn.1001-9081.2017.04.1185

计算机应用 ›› 2017, Vol. 37 ›› Issue (4): 1185-1188.DOI: 10.11772/j.issn.1001-9081.2017.04.1185

• 计算机视觉与虚拟现实 • 上一篇下一篇

人脸识别中基于学习的核图像微分滤波器

房贻广¹, 刘武², 张骥³, 张令臣⁴, 袁玫瑰⁴, 屈磊⁴

1. 国网安徽省电力公司安全监察质量部, 合肥 230022;
2. 国网安庆供电公司安全监察质量部, 安徽安庆 246000;
3. 安徽南瑞继远电网技术有限公司, 合肥 230088;
4. 安徽大学电子信息工程学院, 合肥 230601

收稿日期:2016-08-15 修回日期:2016-12-23 出版日期:2017-04-10 发布日期:2017-04-19
通讯作者: 屈磊
作者简介:房贻广(1969-),男,安徽淮北人,高级工程师,硕士,主要研究方向:电力安全监察;刘武(1978-),男,安徽安庆人,硕士,主要研究方向:电力安全监察;张骥(1982-),男,安徽铜陵人,硕士,主要研究方向:电力视频监控;张令臣(1989-),男,安徽宿州人,硕士,主要研究方向:信号处理、模式识别;袁玫瑰(1991-),女,安徽铜陵人,硕士,主要研究方向:信号处理、模式识别;屈磊(1980-),男,安徽阜阳人,教授,博士,主要研究方向:信号处理、模式识别。
基金资助:
国家自然科学基金资助项目（61201396，61301296）；国家科技支撑计划项目（2014BAH27F01）；国家电网公司科技项目（5212D01502DB）。

Learning based kernel image differential filter for face recognition

FANG Yiguang¹, LIU Wu², ZHANG Ji³, ZHANG Lingchen⁴, YUAN Meigui⁴, QU Lei⁴

1. Safety Supervision Quality Department, State Grid Anhui Electric Power Supply Company, Hefei Anhui 230022, China;
2. Safety Supervision Quality Department, State Grid Anqing Electric Power Supply Company, Anqing Anhui 246000, China;
3. Anhui Jiyuan Electric Power System Technology Company Limited, Hefei Anhui 230088, China;
4. School of Electronics and Information Engineering, Anhui University, Hefei Anhui 230601, China

Received:2016-08-15 Revised:2016-12-23 Online:2017-04-10 Published:2017-04-19
Supported by:
This work is partially supported by National Natural Science Foundation of China (61201396, 61301296), the National Science and Technology Support Program (2014BAH27F01), the Science and Technology Project of China State Grid Corporation (5212D01502DB).

摘要/Abstract

摘要： 针对人脸识别应用，提出一种基于学习且具有鉴别能力的核图像微分滤波器。首先，区别于现有滤波器的手工设计方法，该滤波器利用训练集动态学习获得，通过在学习过程中融入线性判别分析（LDA）思想，可在增加滤波后图像类内相似度的同时减小类间相似度；其次，在线性滤波分类器的基础上进一步引入二阶微分信息，并结合核方法在高维空间下进行滤波器学习，使得图像中的细节和非线性信息可以得到更好的利用并获得更具鉴别力的特征描述。AR和ORL人脸库上的多组对比实验结果表明，与线性可学习图像滤波器IFL、不考虑微分信息的核图像滤波器以及只考虑一阶微分信息的核图像滤波器进行比较，所提算法可有效提高识别性能。

关键词: 滤波器学习, 线性判别分析, 核空间, 二阶微分, 人脸识别

Abstract: For the applications of face recognition, a learning based kernel image differential filter was proposed. Firstly, instead of designing the image filter in a handcrafted or analytical way, the new image filter was designed by dynamically learning from the training data. By integrating the idea of Linear Discriminant Analysis (LDA) into filter learning, the intra-class difference of filtered image was attenuated and the inter-class difference was amplified. Secondly, the second order derivative operator and kernel trick were introduced to better extract the image detail information and cope with the nonlinear feature space problem. As a result, the filter is adaptive and more discriminative feature description can be obtained. The proposed algorithm was experimented on AR and ORL face database and compared with linearly learning image filter named IFL, kernel image filter without differential information, and kernel image filter considering only one order differential information. The experimental results validate the effectiveness of the proposed method.

Key words: filter learning, Linear Discriminant Analysis (LDA), kernel space, second order derivative, face recognition

中图分类号:

TP391.41

房贻广, 刘武, 张骥, 张令臣, 袁玫瑰, 屈磊. 人脸识别中基于学习的核图像微分滤波器[J]. 计算机应用, 2017, 37(4): 1185-1188.

FANG Yiguang, LIU Wu, ZHANG Ji, ZHANG Lingchen, YUAN Meigui, QU Lei. Learning based kernel image differential filter for face recognition[J]. Journal of Computer Applications, 2017, 37(4): 1185-1188.

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人脸识别中基于学习的核图像微分滤波器

Learning based kernel image differential filter for face recognition

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