Face Recognition by Principal Component Regression using Hypercomplex Numbers

In this paper, we propose a classification by principal component regression (CbPCR) strategy, which depends on performing regression of each data class in terms of its principal components. This CbPCR formulation leads to a novel formulation of the Linear Regression Classification (LRC) problem that keeps the key information of the data classes while providing more compact closed-form solutions. We also extend this strategy to the 4D hypercomplex domains to take into account the color information of the image. Our experiments on two color face recognition benchmark databases prove the efficacy of the proposed strategy.