A Comprehensive Empirical Study on Linear Subspace Methods for Facial Expression Analysis
Published 10 July 2006
Caifeng Shan, Shaogang Gong, Peter W. McOwan
Citations50
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TL;DR
This paper systematically examine a number of linear subspace methods for facial expression analysis, and shows that, in comparative studies, the Supervised LPP (SLPP) is superior in supervised methods, while ONPP performs best in unsupervised learning.
Abstract
Automatic facial expression analysis is a vital component of intelligent Human-Computer Interaction (HCI). In this paper, we present a extensive empirical study on linear subspace methods for facial expression analysis. Locality Preserving Projections (LPP) and Orthogonal Neighborhood Preserving Projections (ONPP) are first time applied to facial expression analysis. We systematically examine a number of linear subspace methods, and show that, in our comparative studies, the Supervised LPP (SLPP) is superior in supervised methods, while ONPP performs best in unsupervised learning.
Keywords
PsychologyComputer Science
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