The proposed face recognition algorithm regularises the within-class scatter matrix by extrapolating its eigenvalues. This way both the range space and the null space information of the within-class scatter matrix is utilised. The results are promising when the algorithm is experimented on face recognition datasets and compared with several other methods. In face recognition problems, the dimensionality of the feature space is very large compared to the number of training data samples available. It is therefore necessary to reduce the dimensionality of the space for improving the robustness (or generalisation capability) and computational complexity of the face recognition classifier. Many dimensionality reduction techniques have been proposed in the literature [1, 2] . Among them, the linear discriminant analysis (LDA) technique is perhaps the most widely studied technique for dimensionality reduction. The LDA technique finds an orientation W that transforms high dimensional feature vectors belonging to different classes to a lower dimensional feature space such that the projected feature vectors of a class on this lower dimensional space are well separated from the feature vectors of other classes. In the LDA technique, W can be obtained by the eigenvalue decomposition of the product of the inverse of withinclass scatter matrix S W and between-class scatter matrix S B , i.e. S
In extrapolation LDA (ELDA) technique REF , the null space of S W matrix is regularized by extrapolating eigenvalues of S W using exponential fitting function.
Regularisation of eigenfeatures by extrapolation of scatter-matrix in face-recognition problem
Electronics Letters
Mathematics