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Grassmannian Learning for Facial Expression Recognition from Video


Abstract In this thesis we consider the problem of facial expression recognition (FER) from video sequences. Our method is based on subspace representations and Grassmann manifold based learning. We use Local Binary Pattern (LBP) at the frame level for representing the facial features. Next we develop a model to represent the video sequence in a lower dimensional expression subspace and also as a linear dynamical system using Autoregressive Moving Average (ARMA) model. As these subspaces lie on Grassmann space, we use Grassmann manifold based learning techniques such as kernel Fisher Discriminant Analysis with Grassmann kernels for classification. We consider six expressions namely, Angry (AN), Disgust (Di), Fear (Fe), Happy (Ha), Sadness (Sa) and S... (more)
Created Date 2014
Contributor Yellamraju, Anirudh (Author) / Chakrabarti, Chaitali (Advisor) / Turaga, Pavan (Advisor) / Karam, Lina (Committee member) / Arizona State University (Publisher)
Subject Electrical engineering / Facial Expression Recognition from Video Sequences / Grassmannian learning for Facial Expression Recognition
Type Masters Thesis
Extent 56 pages
Language English
Copyright
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Note Masters Thesis Electrical Engineering 2014
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS


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Description Dissertation/Thesis