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Linear Discriminant Relationship Analysis between All Classes and Class Specified for Face Recognition

Xuegang Wu, Yuan Yan Tang, Bin Fang, Zhaowei Shang, Changyuan Xing


Class specified-based feature extraction method becomes an important reference in classification, such as common vector(CV) algorithm and class frequency analysis(CFA). CV algorithm does not consider the relationship among the different classes, but CFA does while only works on the frequency domain. Furthermore, we found that the relationship among different classes is also important in recognition task. To overcome the disadvantages mentioned above, we proposed an algorithm to adjust the relationship among subspaces of different classes, and at last achieved smaller within-class scatter in each class, larger between-class scatter and closer mean centers in the remaining classes. In the case, features of each class and the relationship with the remaining classes can be shown clearly in its the whole feature subspace. Moreover, it relieves the restriction in a same subspace. We also study the comparable problem in multiple subspaces and reorganize different subspaces in our objective function. The proposed algorithm is an important supplement to CFA and CV in linear discriminant relationship analysis. The experimental results show its advantages in orl and Extend yale B face database. Furthermore, the proposed method is not limited to face recognition, also can be extended to other image-based object recognition.


CFA, Common vector, LDA, Class dependence.

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