A Neighborhood Structure-Preserving Bi-objective Optimization Method Based on Class Center and Discriminant Analysis and Its Application in Facial Recognition

Received: 20 March 2019 Accepted: 25 June 2019 Based on class center and discriminant analysis, this paper puts forward a novel bi-objective optimization method that preserves the neighborhood structure, and applies it to facial recognition. Firstly, the locally preserving projection (LPP) was improved into the class-center locally preserving projection (CLPP) by replacing the sample-based neighborhood structure with the class center-based neighborhood structure. Next, a bi-objective optimization model was developed based on the CLPP and linear discriminant analysis (LDA), and solved by the multi-objective optimization theory. The bi-objective optimization problem combines the merits of single-target CLPP and single-target LDA: the class center-based neighborhood structure is preserved, and the class information is introduced naturally, making up for the defect of the LDA due to the manual changes of adjacency coefficient and highlighting the physical meaning. Finally, several experiments were conducted on AR, CAS-60 and FERET face databases. The experimental results prove that our methods are correct and effective, and the bi-objective optimization method based on CLPP and LDA (CLPP+LDA) achieved the best recognition effect.