Subspace Learning is one of the most useful tools for image analysis and recognition. A large number of such techniques has been proposed utilizing a-priori knowledge about the data. In this paper, new Subspace Learning techniques are presented that use symmetry constraints in their objective functions. The rational behind this idea is to exploit the apriori knowledge that geometrical symmetry appears in several types of data, such as images, objects, faces, etc. Experiments on artificial, facial expression recognition, face recognition and object categorization databases highlight the superiority and the robustness of the proposed techniques, in comparison to standard Subspace Learning techniques.
- Subspace learning
- symmetry constraints
- principal component analysis (PCA)
- Linear Discriminant analysis (LDA)
- clustering based discriminant analysis (CDA)