Computer Engineering and Applications ›› 2013, Vol. 49 ›› Issue (23): 153-156.

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Face recognition with unitary regression classification based on minimizing TPE

WANG Junqin   

  1. School of Physics and Mechanical & Electronic Engineering, Xi’an University of Arts and Science, Xi’an 710065, China
  • Online:2013-12-01 Published:2016-06-12

最小化TPE一元回归分类在人脸识别中的应用

王军琴   

  1. 西安文理学院 物理与机械电子工程学院,西安 710065

Abstract: For the issue that existing regression classification methods in face recognition do not consider total projection error within classes well, a unitary regression classification method based on minimizing Total Projection Error(TPE) is proposed. Characteristics decomposition is used to find unitary rotation matrix after the projection error matrix within class of all the training data is calculated by projection matrix of each class. Then, unitary rotation matrix is used to convert each training image vector to a new vector space, and the specific projection matrix of each class is worked out. Minimum projection error of each class in unitary rotating subspace is used to finish face recognition. The effectiveness and robustness of proposed method has been verified by experiments on the two common face databases FEI and FERET. Experimental results show that proposed method has better recognition accuracy than several other advanced regression classification approaches.

Key words: face recognition, minimizing total projection error, linear regression classification, unitary regression classification, unitary rotate subspace

摘要: 针对人脸识别中现有回归分类方法不能很好地考虑总类内投影误差的问题,提出了一种基于最小化总投影误差(TPE)的一元回归分类方法。通过各个类投影矩阵计算所有训练数据的类内投影误差矩阵,并且借助特征分解找到一元旋转矩阵;利用一元旋转矩阵将每个训练图像向量转换为新的向量空间,并计算出每个类的特定投影矩阵;根据一元旋转子空间中各个类的最小投影误差来完成人脸的识别。在两大通用人脸数据库FEI和FERET上的实验验证了所提方法的有效性及鲁棒性,实验结果表明,相比其他几种先进的回归分类方法,所提方法取得了更好的识别效果。

关键词: 人脸识别, 最小化总投影误差, 线性回归分类, 一元回归分类, 一元旋转子空间