Facial Expression Detection using Patch-based Eigen-face Isomap Networks
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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 and 66-73 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.
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