Automatic Feature Localization in Thermal Images for Facial Expression Recognition

Citations Over TimeTop 10% of 2006 papers

Abstract

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.

Related Papers

• → A COMPARATIVE STUDY ON THRESHOLDING TECHNIQUES FOR GRAY IMAGE BINARIZATION(2017)11 cited
• → Multi-level thresholding and its application to feature extraction in machine parts(2005)1 cited
• → Global Thresholding and Multiple Pass Parsing(1997)47 cited
• → A localized thresholding method based on boundary detection(2004)