NDLI: Latent semantic analysis of facial action codes for automatic facial expression recognition

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Latent semantic analysis of facial action codes for automatic facial expression recognition

Content Provider	ACM Digital Library
Author	Monay, Florent Gatica-Perez, Daniel Fasel, Beat
Abstract	For supervised training of automatic facial expression recognition systems, adequate ground truth labels that describe relevant facial expression categories are necessary. One possibility is to label facial expressions into emotion categories. Another approach is to label facial expressions independently from any interpretation attempts. This can be achieved via the facial action coding system (FACS). In this paper we present a novel approach that allows to automatically cluster FACScodes into meaningful categories. Our approach exploits the fact that FACScodes can be seen as documents containing terms -the action units (AUs) present in the codes-and so text modeling methods that capture co-occurrence information in low-dimensional spaces can be used. The FACScode derived descriptions are computed by Latent Semantic Analysis (LSA) and Probabilistic Latent Semantic Analysis (PLSA). We show that, as a high-level description of facial actions, the newly derived codes constitute a competitive alternative to both basic emotion and FACScodes. We have used them to train different types of artificial neural networks.
Starting Page	181
Ending Page	188
Page Count	8
File Format	PDF
ISBN	1581139403
DOI	10.1145/1026711.1026742
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2004-10-15
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Automatic facial expression recognition Latent semantic analysis
Content Type	Text
Resource Type	Article

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