Improved 2DLDA Algorithm and Its Application in Face Recognition
Dong Wang, Shunfang Wang · 2014
The face recognition method based on linear discriminant analysis (LDA) is always faced with the high-dimensional small sample size problem in image processing. The two-dimensional linear discriminant analysis (2DLDA) which was proposed recently can extract the feature from the original image matrix directly and decrease the dimensionality of the original matrix in a great extent. But there are still some problems such as the overlap of the neighbor samples and the deviation of the class-center. This paper improves the 2DLDA algorithm and proposes the MF2DLDA algorithm. The MF2DLDA algorithm improves the recognition rate by redefining the between-class scatter matrix which can retains the most discriminative features and using the median matrix instead of the mean matrix which can weaken the negative effect that the anomalous data has on the computing of the median matrix. The results of the experiments show that the new algorithm is feasible.