Feature extraction using fuzzy inverse FDA

摘要

This paper proposes a new method of feature extraction and recognition, namely, the fuzzy inverse Fisher discriminant analysis (FIFDA) based on the inverse Fisher discriminant criterion and fuzzy set theory. In the proposed method, a membership degree matrix is calculated using FKNN, then the membership degree is incorporated into the definition of the between-class scatter matrix and within-class scatter matrix to get the fuzzy between-class scatter matrix and fuzzy within-class scatter matrix. Experimental results on the ORL, FERET face databases and pulse signal database show that the new method outperforms Fisherface, fuzzy Fisherface and inverse Fisher discriminant analysis.