Automatic Feature Localization in Thermal Images for Facial Expression Recognition

We propose an unsupervised Local and Global feature extraction paradigm to approach the problem of facial expression recognition in thermal images. Starting from local, low-level features computed at interest point locations, our approach combines the localization of facial features with the holisti...

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Bibliographic Details
Published inIEEE Computer Society Conference on Computer Vision and Pattern Recognition workshops p. 14
Main Authors Trujillo, L., Olague, G., Hammoud, R., Hernandez, B.
Format Conference Proceeding
LanguageEnglish
Japanese
Published IEEE 2005
Subjects
Data mining
Face detection
Face recognition
Facial features
Feature extraction
Humans
Image analysis
Image recognition
Information analysis
Support vector machines
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Summary:We propose an unsupervised Local and Global feature extraction paradigm to approach the problem of facial expression recognition in thermal images. Starting from local, low-level features computed at interest point locations, our approach combines the localization of facial features with the holistic approach. The detailed steps are as follows: First, face localization using bi-modal thresholding is accomplished in order to localize facial features by way of a novel interest point detection and clustering approach. Second, we compute representative Eigenfeatures for feature extraction. Third, facial expression classification is made with a Support Vector Machine Committiee. Finally, the experiments over the IRIS data-set show that automation was achieved with good feature localization and classification performance.
ISBN:9780769526607
0769526608
ISSN:2160-7508
DOI:10.1109/CVPR.2005.415