Discriminant Functional Learning of Color Features for the Recognition of Facial Action Units and Their Intensities

Color is a fundamental image feature of facial expressions. For example, when we furrow our eyebrows in anger, blood rushes in, turning some face areas red; or when one goes white in fear as a result of the drainage of blood from the face. Surprisingly, these image properties have not been exploited to recognize the facial action units (AUs) associated with these expressions. Herein, we present the first system to do recognition of AUs and their intensities using these functional color changes. These color features are shown to be robust to changes in identity, gender, race, ethnicity, and skin color. Specifically, we identify the chromaticity changes defining the transition of an AU from inactive to active and use an innovative Gabor transform-based algorithm to gain invariance to the timing of these changes. Because these image changes are given by functions rather than vectors, we use functional classifiers to identify the most discriminant color features of an AU and its intensities. We demonstrate that, using these discriminant color features, one can achieve results superior to those of the state-of-the-art. Finally, we define an algorithm that allows us to use the learned functional color representation in still images. This is done by learning the mapping between images and the identified functional color features in videos. Our algorithm works in realtime, i.e., 30 frames/second/CPU thread.