Combining AAM coefficients with LGBP histograms in the multi-kernel SVM framework to detect facial action units

Abstract

This study presents a combination of geometric and appearance features used to automatically detect Action Units in face images. We use one multi-kernel SVM for each Action Unit we want to detect. The first kernel matrix is computed using Local Gabor Binary Pattern (LGBP) histograms and a histogram intersection kernel. The second kernel matrix is computed from AAM coefficients and a RBF kernel. During the training step, we combine these two type s of features using the recent SimpleMKL algorithm. SVM outputs are then filtered to exploit dynamic relationships between Action Units.