  Automated facial expression detection problem pose two primary challenges
that include variations in expression and facial occlusions (glasses, beard,
mustache or face covers). In this paper we introduce a novel automated patch
creation technique that masks a particular region of interest in the face,
followed by Eigen-value decomposition of the patched faces and generation of
Isomaps to detect underlying clustering patterns among faces. The proposed
masked Eigen-face based Isomap clustering technique achieves 75% sensitivity
and 66-73% accuracy in classification of faces with occlusions and smiling
faces in around 1 second per image. Also, betweenness centrality, Eigen
centrality and maximum information flow can be used as network-based measures
to identify the most significant training faces for expression classification
tasks. The proposed method can be used in combination with feature-based
expression classification methods in large data sets for improving expression
classification accuracies.
