The Detection of Concept Frames Using Clustering Multi-instance Learning

The classification of sequences requires the combination of information from different time points. In this paper the detection of facial expressions is considered. Experiments on the detection of certain facial muscle activations in videos show that it is not always required to model the sequences...

Full description

Saved in:
Bibliographic Details
Main Authors: Tax, D M J, Hendriks, E, Valstar, M F, Pantic, M
Format: Conference Proceeding
Language:English
Subjects:
classification
Data models
Gold
Hidden Markov models
Logistics
multi-instance learning
Pattern recognition
Time series analysis
time series classification
Training
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The classification of sequences requires the combination of information from different time points. In this paper the detection of facial expressions is considered. Experiments on the detection of certain facial muscle activations in videos show that it is not always required to model the sequences fully, but that the presence of specific frames (the concept frame) can be sufficient for a reliable detection of certain facial expression classes. For the detection of these concept frames a standard classifier is often sufficient, although a more advanced clustering approach performs better in some cases.
ISSN:1051-4651
2831-7475
DOI:10.1109/ICPR.2010.715