A Face Recognition Algorithm Using a Fusion Method Based on Resampling Bidirectional 2DLDA

LI Wen-hui; JIANG Yuan-yuan; WANG Ying; FU Bo

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A Face Recognition Algorithm Using a Fusion Method Based on Resampling Bidirectional 2DLDA

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- A Face Recognition Algorithm Using a Fusion Method Based on Resampling Bidirectional 2DLDA
- Acta Electronica Sinica Vol. 39, Issue 11, Pages: 2526-2533(2011)
- 作者机构：
  
  吉林大学计算机科学与技术学院,吉林,长春,130012
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP391.4
- Published：2011
- 稿件说明：
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LI Wen-hui, JIANG Yuan-yuan, WANG Ying, et al. A Face Recognition Algorithm Using a Fusion Method Based on Resampling Bidirectional 2DLDA[J]. Acta Electronica Sinica, 2011, 39(11): 2526-2533.
DOI：

LI Wen-hui, JIANG Yuan-yuan, WANG Ying, et al. A Face Recognition Algorithm Using a Fusion Method Based on Resampling Bidirectional 2DLDA[J]. Acta Electronica Sinica, 2011, 39(11): 2526-2533. DOI：

摘要

针对人脸识别中普遍存在的光照、表情等变化带来的识别问题和小样本问题

本文提出了一种利用重采样技术融合双向2DLDA特征的人脸识别算法Resampling Bidirection 2DLDA(RB2DLDA).二维线性判别分析中

2DLDA利用垂直方向上的类内和类间协方差信息进行识别

E2DLDA利用水平方向上的类内和类间协方差信息进行识别

本文中从理论上证明了这两个方向上的判别信息具有一定的互补性

为融合两个方向的判别信息进行分类器的设计

改善分类器的识别性能提供了理论基础.同时为RB2DLDA算法提出一种自适应的降维参数设定方法

经过在AR和CAS-PEAL-R1人脸库上的实验表明

RB2DLDA算法具有较高的识别率和鲁棒性.

Abstract

Aiming at recognition problem caused by variations in lighting

facial expression etc and small sample size problem which generally exist in face recognition

this paper proposes a novel recognition method

called RB2DLDA

which fuses bidirectional 2DLDA feature by resampling technique.In matrix-based LDA

2DLDA uses inner-class and intra-class covariance information in the vertical direction

and E2DLDA uses inner-class and intra-class covariance information in the horizontal direction

this paper proves in theory that class information in the two directions is complementary to each other to some extent

provides theoretical foundation for fusing class information in the two directions to design classifier and improves recognition performance of the classifier.Moreover

proposes an adaptive algorithm of setting feature reduction parameter.Our method is tested on AR and CAS-PEAL-R1 face databases

and the experimental results show that the proposed approach possesses higher recognition accuracy and robustness than other state-of-the art methods.

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