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.

本文提出了一种基于面部特定区域遮挡的自动化修补方法，将远程真实世界中的表情分类的问题转换为图像分类问题，并使用 Isomap 聚类技术来检测在修补后的图像中存在的面部表情模式。该方法可用于大数据集中与基于特征的表情分类方法相结合以提高表情分类准确度。