Facial Expression Detection using Patch-based Eigen-face Isomap Networks
This work addresses facial expression detection for applications like human-computer interaction, but it is incremental as it builds on existing patch-based and network methods.
The paper tackled automated facial expression detection by addressing variations in expression and facial occlusions, achieving 75% sensitivity and 66-73% accuracy in classification with processing times of about 1 second per image.
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.