Abstract

                            Facial Expression recognition using subspace processing involves a sequence of actions on the image data and the generation of output which gives the performance range of recognition. By contrast, real time recognition of facial expression systems using subspace methods is often event driven with minimal feature extraction data processing. In this paper dimensionality reduction subspace analysis are illustrated. Extraction of feature depends on dimensionality reduction vectors which gives the comparison information’s required for classifying the various facial expression images. Emotion variations can be analyzed using subspace methods. This brief survey gives differences between various subspace methods.

Keyword: Subspace, Facial expression recognition, Principal component, Independent component