Inverted Sparse Discriminant Preserving Projection for Face Recognition

Abstract

Image classification and face recognition has been a popular subject matter for the last several decades. Images are usually handled as transformed as vectors which makes their classification a dimensionality reduction task. Some of the well-known algorithms in the area, such as the Sparsity Preserving Projection (SPP), create new theoretical concepts families, while other successfully modify or combine useful properties of the former ones. Compiled algorithms like Sparse Discriminant Preserving Projections (SDPP) employ the properties of the Sparse Representation (SR) as in their objective functions they include a supervised modification of the sparse weight matrix that considers the intra-class relations. By examining the construction of the SDPP algorithm and by providing some arguments on the supervised SR, in this paper we propose a new subspace learning algorithm, called Inverted Sparse Discriminant Preserving Projection (ISDPP). Likewise SDPP, ISDPP integrates supervised SR with the Fisher’s criterion. In contrast to SDPP, ISDPP incorporates a between-class SR with the Fischer’s within-class scatter matrix. A preliminary round of experiments support the initiative and provide an expectation for possible superior performance of the proposed ISDPP that is confirmed in the next round of empirical examinations.