Facial Action Coding System Research Papers (original) (raw)

1. In the present research, we test the assumption that emotional mimicry and contagion are moderated by group membership. We report two studies using facial electromyography (EMG; Study 1), Facial Action Coding System (FACS; Study 2),... more

1. In the present research, we test the assumption that emotional mimicry and contagion are moderated by group membership. We report two studies using facial electromyography (EMG; Study 1), Facial Action Coding System (FACS; Study 2), and self-reported emotions (Study 2) as dependent measures. As predicted, both studies show that ingroup anger and fear displays were mimicked to a greater extent than outgroup displays of these emotions. The self-report data in Study 2 further showed specific divergent reactions ...

Many approaches to facial expression recognition focus on assessing the six basic emotions (anger, disgust, happiness, fear, sadness, and surprise). Real-life situations proved to produce many more subtle facial expressions. A reliable... more

Many approaches to facial expression recognition focus on assessing the six basic emotions (anger, disgust, happiness, fear, sadness, and surprise). Real-life situations proved to produce many more subtle facial expressions. A reliable way of analyzing the facial behavior is the Facial Action Coding System (FACS) developed by Ekman and Friesen, which decomposes the face into 46 action units (AU) and is usually performed by a human observer. Each AU is related to the contraction of one or more specific facial muscles. In this study we present an approach towards automatic AU recognition enabling recognition of an extensive palette of facial expressions. As distinctive features we used motion flow estimators between every two consecutive frames, calculated in special regions of interest (ROI). Even though a lot has been published on the facial expression recognition theme, it is still difficult to draw a conclusion regarding the best methodology as there is no common basis for compari...

Facial expression has been a focus of emotion research for over a hundred years (Darwin, 1872/1998). It is central to several leading theories of emotion (Ekman, 1992; Izard, 1977; Tomkins, 1962) and has been the focus of at times heated... more

Facial expression has been a focus of emotion research for over a hundred years (Darwin, 1872/1998). It is central to several leading theories of emotion (Ekman, 1992; Izard, 1977; Tomkins, 1962) and has been the focus of at times heated debate about issues in emotion science (Ekman, 1973, 1993; Fridlund, 1992; Russell, 1994). Facial expression figures prominently in research on almost every aspect of emotion, including psychophysiology (Levenson, Ekman, & Friesen, 1990), neural bases (Calder et al., 1996; Davidson, Ekman, ...

The facial action coding system (FACS) was used to examine recognition rates in 105 healthy young men and women who viewed 128 facial expressions of posed and evoked happy, sad, angry and fearful emotions in color photographs balanced for... more

The facial action coding system (FACS) was used to examine recognition rates in 105 healthy young men and women who viewed 128 facial expressions of posed and evoked happy, sad, angry and fearful emotions in color photographs balanced for gender and ethnicity of poser. Categorical analyses determined the specificity of individual action units for each emotion. Relationships between recognition rates for different emotions and action units were evaluated using a logistic regression model. Each emotion could be identified by a group of action units, characteristic to the emotion and distinct from other emotions. Characteristic happy expressions comprised raised inner eyebrows, tightened lower eyelid, raised cheeks, upper lip raised and lip corners turned upward. Recognition of happy faces was associated with cheek raise, lid tightening and outer brow raise. Characteristic sad expressions comprised furrowed eyebrow, opened mouth with upper lip being raised, lip corners stretched and turned down, and chin pulled up. Only brow lower and chin raise were associated with sad recognition. Characteristic anger expressions comprised lowered eyebrows, eyes wide open with tightened lower lid, lips exposing teeth and stretched lip corners. Recognition of angry faces was associated with lowered eyebrows, upper lid raise and lower lip depression. Characteristic fear expressions comprised eyes wide open, furrowed and raised eyebrows and stretched mouth. Recognition of fearful faces was most highly associated with upper lip raise and nostril dilation, although both occurred infrequently, and with inner brow raise and widened eyes. Comparisons are made with previous studies that used different facial stimuli.

Abstract We show how high-level scene properties can be in-ferred from classification of low-level image features, specifically for the indoor-outdoor scene retrieval prob-lem. We systematically studied the features: (1) his-tograms in... more

Abstract We show how high-level scene properties can be in-ferred from classification of low-level image features, specifically for the indoor-outdoor scene retrieval prob-lem. We systematically studied the features: (1) his-tograms in the Ohta color space (2) multiresolution, si- ...

We develop an automatic system to analyze subtle changes in upper face expressions based on both permanent facial features (brows, eyes, mouth) and transient facial features (deepening of facial furrows) in a nearly frontal image... more

We develop an automatic system to analyze subtle changes in upper face expressions based on both permanent facial features (brows, eyes, mouth) and transient facial features (deepening of facial furrows) in a nearly frontal image sequence. Our system recognizes fine-grained changes in facial expression based on Facial Action Coding System (FACS) action units (AUs). Multi-state facial component models are proposed for tracting and modeling different facial features, including eyes, brews, cheeks, and furrows. Then we ...

We present a systematic comparison of machine learning methods applied to the problem of fully automatic recognition of facial expressions. We explored recognition of facial actions from the Facial Action Coding System (FACS), as well as... more

We present a systematic comparison of machine learning methods applied to the problem of fully automatic recognition of facial expressions. We explored recognition of facial actions from the Facial Action Coding System (FACS), as well as recognition of full facial expressions. Each videoframe is first scanned in real-time to detect approximately upright-frontal faces. The faces found are scaled into image patches of equal size, convolved with a bank of Gabor energy filters, and then passed to a recognition engine that codes facial expressions into 7 dimensions in real time: neutral, anger, disgust, fear, joy, sadness, surprise. We report results on a series of experiments comparing recognition engines, including AdaBoost, support vector machines, linear discriminant analysis, as well as feature selection techniques. Best results were obtained by selecting a subset of Gabor filters using AdaBoost and then training Support Vector Machines on the outputs of the filters selected by AdaB...