This paper is motivated by Ellgring's work in non-verbal communication in depression to measure and compare the levels of facial activity, before and after treatment, of endogenous and neurotic depressives. Similar to that work, we loosely associate the measurements with Action Units (AU) groups from the Facial Action Coding System (FACS). However, we use the neologism Region Units (RU) to describe regions of the face that encapsulate AUs. In contrast to Ellgring's approach, we automatically generate the measurements and provide both prototypical expression recognition and RU-specific activity measurements. Latency between expressions is also measured and the system is conducive to comparison across groups and individual subjects. By using Active Appearance Models (AAM) to locate the fiduciary facial points, and MultiBoost to classify prototypical expressions and the RUs, we can provide a simple, objective, flexible and cost-effective means of automatically measuring facial activity.
Abstract. This paper describes some of the issues faced by typical emotion recognition systems and the need to be able to deal with emotions in a natural setting. Studies tend to ignore the dynamic, versatile and personalised nature of affective expression and the influence that social setting, context and culture have on its rules of display. Affective cues can be present in multiple modalities and they can manifest themselves in different temporal order. Thus, fusing the feature sets is challenging. We present a composite approach to affective sensing. The term composite is used to reflect the blending of information from multiple modalities with the available semantic evidence to enhance the emotion recognition process.
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