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Characterizing facial expressions by grammars of action unit sequences: a first investigation using ABL
- Publication Year :
- 2016
-
Abstract
- We investigate the application of grammar inference to the analysis of facial expressions to discover underlying sequential regularities characteristic for a specific mental state. The input consists of sequences of action units (AUs), which represent basic facial signals. The typical classification task for facial expression analysis is to assign a set of AUs its corresponding mental state, e.g., an emotion. To our knowledge, there is no research investigating whether there is diagnostic information in the sequence in which the AUs occur in a given time interval. Our study is based on data of facial expressions of pain obtained in a psychological experiment with 347 pain episodes of 86 subjects represented as sequences of AUs. We applied the Alignment-Based Learning (ABL) approach to infer the underlying grammar for the set of all AUs which occurred in the sequences and for a reduced alphabet of the relevant AUs only. We used 10-fold cross-validation to estimate performance and we extended ABL with a frequency-based heuristics to reduce the number of grammar rules by eliminating such rules which do not contribute significantly to performance. The resulting grammar for the reduced AU alphabet provides a first approximation for a "grammar of pain".
- Subjects :
- Information Systems and Management
Computer science
Speech recognition
media_common.quotation_subject
education
02 engineering and technology
Interval (mathematics)
Theoretical Computer Science
Task (project management)
03 medical and health sciences
0302 clinical medicine
Rule-based machine translation
Artificial Intelligence
0202 electrical engineering, electronic engineering, information engineering
Set (psychology)
media_common
Sequence
Facial expression
Grammar
Grammar inference
Computer Science Applications
Control and Systems Engineering
020201 artificial intelligence & image processing
Alphabet
030217 neurology & neurosurgery
Software
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....7e73d129cb7c19eb5bffb89fbb297fdc