Fusing Global and Local Complete Linear Discriminant Features by Fuzzy Integral for Face Recognition

Face recognition becomes very difficult in a complex environment, and the combination of multiple classifiers is a good solution to this problem. A novel face recognition algorithm GLCFDA-FI is proposed in this paper, which fuses the complementary information extracted by complete linear discriminant analysis from the global and local features of a face to improve the performance. The Choquet fuzzy integral is used as the fusing tool due to its suitable properties for information aggregation. Experiments are carried out on the CAS-PEAL-R1 database, the Harvard database and the FERET database to demonstrate the effectiveness of the proposed method. Results also indicate that the proposed method GLCFDA-FI outperforms five other commonly used algorithms — namely, Fisherfaces, null space-based linear discriminant analysis (NLDA), cascaded-LDA, kernel-Fisher discriminant analysis (KFDA), and null-space based KFDA (NKFDA).