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.

Facial color is an efficient mechanism to visually transmit emotion.

Facial expressions of emotion in humans are believed to be produced by contracting one's facial muscles, generally called action units. However, the surface of the face is also innervated with a large network of blood vessels. Blood flow variations in these vessels yield visible color changes on the face.

The not face: A grammaticalization of facial expressions of emotion.

Facial expressions of emotion are thought to have evolved from the development of facial muscles used in sensory regulation and later adapted to express moral judgment. Negative moral judgment includes the expressions of anger, disgust and contempt. Here, we study the hypothesis that these facial expressions of negative moral judgment have further evolved into a facial expression of negation regularly used as a grammatical marker in human language.

Multiobjective optimization for model selection in kernel methods in regression.

Regression plays a major role in many scientific and engineering problems. The goal of regression is to learn the unknown underlying function from a set of sample vectors with known outcomes. In recent years, kernel methods in regression have facilitated the estimation of nonlinear functions.

Discriminant features and temporal structure of nonmanuals in American Sign Language.

To fully define the grammar of American Sign Language (ASL), a linguistic model of its nonmanuals needs to be constructed. While significant progress has been made to understand the features defining ASL manuals, after years of research, much still needs to be done to uncover the discriminant nonmanual components. The major barrier to achieving this goal is the difficulty in correlating facial features and linguistic features, especially since these correlations may be temporally defined.

Salient and Non-Salient Fiducial Detection using a Probabilistic Graphical Model.

Deformable shape detection is an important problem in computer vision and pattern recognition. However, standard detectors are typically limited to locating only a few salient landmarks such as landmarks near edges or areas of high contrast, often conveying insufficient shape information. This paper presents a novel statistical pattern recognition approach to locate a dense set of salient and non-salient landmarks in images of a deformable object.