Random Projection with Robust Linear Discriminant Analysis Model in Face Recognition

This paper presents a face recognition technique with two techniques: random projection (RP) and robust linear discriminant analysis model (RDM). RDM is an enhanced version of fisher's linear discriminant with energy-adaptive regularization criteria. It is able to yield better discrimination performance. Same as Fisher's Linear Discriminant, it also faces the singularity problem of within-class scatter. Thus, a dimensionality reduction technique, such as principal component analysis (PCA), is needed to deal with this problem. In this paper, RP is used as an alternative to PCA in RDM in the application of face recognition. Unlike PCA, RP is training data independent and the random subspace computation is relatively simple. The experimental results illustrate that the proposed algorithm is able to attain better recognition performance (error rate is approximately 5% lower) compared to Fisherfaces.