Automatic Feature Localization in Thermal Images for Facial Expression Recognition

• Published in: IEEE 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
• Language: English; 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
• DOI: 10.1109/CVPR.2005.415
• ISBN: 9780769526607; 0769526608
• ISSN: 2160-7508

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.