Multilinear local fisher discriminant analysis for face recognition

Abstract

In this paper, a multilinear local fisher discriminant analysis (MLFDA) framework is introduced for tensor object dimensionality reduction and recognition. MLFDA achieves feature extraction by finding a multilinear projection to map the original tensor space into a tensor subspace that maximize the local between-class scatter as well as minimize the local within-class scatter. The experimental result shows that MLFDA has an outperformance.