Automatic Facial Action Detection Using Histogram Variation Between Emotional States

This article presents an appearance based method to detect automatically facial actions. Our approach focuses on reducing features sensitivity to identity of the subject. We compute from an expressive image a Local Gabor Binary Pattern (LGBP) histogram and synthesize a LGBP histogram approaching the one we would compute on a neutral face. Difference between these two histograms are used as inputs of Support Vector Machine (SVM) binary detectors associated with a new kernel: the Histogram Difference Intersection (HDI) kernel. Experimental results carried out for 16 Action Units (AUs) on the benchmark Cohn-Kanade database can be compared favorably with two state-of-the-art methods.