Abstract—When the feature dimension is larger than the number of samples the small sample-size problem occurs. There is great concern about it within the face recognition community. We point out that optimizing the Fisher index in linear discriminant analysis does not necessarily give the best performance for a face recognition system. We propose a new regularization scheme. The proposed method is evaluated using the Olivetti Research Laboratory database, the Yale database, and the Feret database. Index Terms—Face recognition, optimization, regularized discriminant analysis (RDA), small sample-size problem. I.

In this paper, we make a study on three Linear Discriminant Analysis (LDA) based

Linear discriminant analysis (LDA) is a widely-used feature extraction method in classification. However, the original LDA has limitations due to the assumption of a unimodal structure for each cluster, which is not satisfied in many applications such as facial image data when variations, e.g. angle and illumination, can significantly influence the images. In this paper, we propose a novel method called hierarchical LDA (h-LDA), which takes into account hierarchical subcluster structures of the data in the LDA formulation and algorithm. We develop a theoretical basis of hierarchical LDA by identifying its relation to two-way multivariate analysis of variance (MANOVA) based on the data model and variance decomposition. Furthermore, an efficient algorithm for a regularized version of h-LDA (h-RLDA) is presented using the QR decomposition and the generalized SVD. To validate the effectiveness of the proposed method, we compare face recognition performance among h-RLDA, LDA, PCA, and TensorFaces. Our experiments show that h-RLDA produces better prediction accuracy than other methods. When only a small subset of features are used (reduced dimensionality), the superiority of h-RLDA over other methods becomes more significant. It is also shown that h-RLDA is a computationally much more efficient alternative to TensorFaces.

Illumination variation is a challenging problem in face recognition research area. Same person can appear greatly different under varying lighting conditions. This paper consists of Face Recognition System which is invariant to illumination variations. Face recognition system which uses Linear Discriminant Analysis (LDA) as feature extractor have Small Sample Size (SSS). It consists of

Linear discriminant analysis (LDA) is a widely-used feature extraction method in classification. However, the original LDA has limitations due to the assumption of a unimodal structure for each cluster, which is satisfied in many applications such as facial image data when variations such as angle and illumination can significantly influence the images of the same person. In this paper, we propose a novel method, hierarchical LDA(h-LDA), which takes into account hierarchical subcluster structures in the data sets. Our experiments show that regularized h-LDA produces better accuracy than LDA, PCA, and tensorFaces. 1