代价敏感的监督流形学习人脸识别方法

系统仿真学报 ›› 2016, Vol. 28 ›› Issue (5): 1077-1085.

代价敏感的监督流形学习人脸识别方法

崔业勤, 高建国

廊坊师范学院数学与信息科学学院,河北廊坊 065000

收稿日期:2014-09-25 修回日期:2015-05-08 发布日期:2020-07-03
作者简介:崔业勤(1973-), 女, 河北廊坊, 硕士, 副教授, 研究方向为计算机视觉、模式识别与人工智能; 高建国(1970-), 男, 河北廊坊, 硕士, 副教授, 研究方向为数据库、图形图像处理与机器学习理论。

Face Recognition Method Based on Cost-Sensitive Supervised Manifold Learning

Cui Yeqin, Gao Jianguo

College of Mathematics and Information Science, Langfang Normal University, Langfang 065000, China

Received:2014-09-25 Revised:2015-05-08 Published:2020-07-03

摘要/Abstract

摘要： 基于子空间学习的人脸识别均假设所有错误识别会导致一样的损失。在人脸识别应用中,不同的错误识别造成的损失则不同。提出一种代价敏感的监督流形学习人脸识别方法,该方法采用一个代价矩阵来指定不同的误分类代价,并将其容纳到局部保持投影(Locality Preserving Projections,LPP)算法中,获得相应的代价敏感局部保持投影(Cos-Sen LPP),以实现人脸识别整体损失最小化。在3个人脸数据库上的实验结果表明,与现有的子空间学习方法相比,Cos-Sen LPP方法花费了最少的整体代价。

关键词: 代价敏感, 流形学习, 人脸识别, 局部保持投影

Abstract: Existing subspace learning-based face recognition methods assume the same loss from all misclassifications. In the real-world face recognition applications, however, different misclassifications can lead to different losses. Motivated by this concern, a cost-sensitive supervised manifold learning approach for face recognition was proposed. The proposed approach incorporated a cost matrix to specify the different costs associated with misclassifications of subjects, into locality preserving projection algorithm, which devised the corresponding cost-sensitive methods, namely, cost-sensitive locality preserving projections (Cos-Sen LPP), to achieve a minimal overall loss. Three face databases were put into the experiments and experimental results show that Cos-Sen LPP method can achieve minimal cost than existing subspace learning-based face recognition methods.

Key words: cost-sensitive, manifold learning, face recognition, Locality Preserving Projections (LPP)

中图分类号:

TP391

崔业勤, 高建国. 代价敏感的监督流形学习人脸识别方法[J]. 系统仿真学报, 2016, 28(5): 1077-1085.

Cui Yeqin, Gao Jianguo. Face Recognition Method Based on Cost-Sensitive Supervised Manifold Learning[J]. Journal of System Simulation, 2016, 28(5): 1077-1085.

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