Facial Expression Recognition Using Facial Features and Manifold Learning

This paper explores robust facial expression recognition techniques based on the underlying low dimensional manifolds embedded in facial images of varying expression. Faces are automatically detected and facial features are extracted, normalized and mapped onto a low dimensional projection surface using Locality Preserving Projections. Alternatively, processed image pixels are used for manifold construction. Classification models robustly estimate expression from the low dimensional projections in manifold space. This method performs robustly in natural settings, enabling more engaging human computer interfaces.