Face recognition: Comparative study between linear and non linear dimensionality reduction methods

In the field of face recognition, the major challenge that encountered classification algorithms, is to deal with the high dimensionality of the space representing data faces. Many methods have been used to solve the issue, our focus, in this paper, is to compare the efficiency (in the term of complexity and recognition rate) of linear and non linear dimensionality reduction methods. We study the influence of high and low dimensionality of features using PCA, LDA, ICA and Sparse Random Projection. Experiments show that projecting the data onto a lower-dimensional subspace using non linear method give a high face recognition rate.