Face recognition algorithm based on modular 2DPCA and CCLDA

Computer Engineering and Applications ›› 2014, Vol. 50 ›› Issue (12): 129-132.

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Face recognition algorithm based on modular 2DPCA and CCLDA

FENG Huali1, LIU Yuan2

1.Education Information Center, Wuxi Institute of Commerce and Technology, Wuxi, Jiangsu 214153, China
2.School of Digital Media, Jiangnan University, Wuxi, Jiangsu 214122, China

Online:2014-06-15 Published:2015-05-08

M2DPCA与CCLDA相结合的人脸识别

冯华丽1，刘渊2

1.无锡商业职业技术学院教育信息化中心，江苏无锡 214153
2.江南大学数字媒体学院，江苏无锡 214122

Abstract

Abstract: An improved face recognition algorithm is proposed based on the combination of modular 2DPCA and Contextual Constraints based Linear Discriminant Analysis（CCLDA） because of the disadvantages of CCLDA. CCLDA first transforms an image matrix to a vector which causes high dimensionality and computational complexity and not considers the local feature. Experimental results obtained on ORL and XM2VTS databases show the effectiveness of the new method.

Key words: contextual constraints, Modular 2-Dimensional Principal Component Analysis（M2DPCA）, Contextual Constraints based Linear Discriminant Analysis（CCLDA）, face recognition

摘要： CCLDA算法将图像矩阵转化为向量进行处理，该算法易造成数据维数很大，计算量复杂并容易出现“小样本”等问题。针对以上这些问题，提出了一种基于模块化2DPCA和CCLDA相结合的协同处理方法并应用于人脸识别领域。并且在ORL和XM2VTS人脸库上的实验结果表明，新方法在识别效果上有比以往的算法更为明显的优势。

关键词: 上下文约束, 模块化二维主成分分析（M2DPCA）, 基于上下文约束线性判别分析（CCLDA）, 人脸识别

FENG Huali1, LIU Yuan2. Face recognition algorithm based on modular 2DPCA and CCLDA[J]. Computer Engineering and Applications, 2014, 50(12): 129-132.

冯华丽1，刘渊2. M2DPCA与CCLDA相结合的人脸识别[J]. 计算机工程与应用, 2014, 50(12): 129-132.

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Face recognition algorithm based on modular 2DPCA and CCLDA

M2DPCA与CCLDA相结合的人脸识别

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