Modeling naturalistic affective states via facial

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Naturalistic affective expressions change at a rate much slower than the typical rate at which video or audio is recorded. This increases the probability that consecutive recorded instants of expressions represent the same affective content. In this paper, we exploit such a relationship to improve the recognition performance of continuous naturalistic affective expressions.

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Emotion can be defined as any relatively brief conscious experience characterized by intense mental activity and a high degree of pleasure or displeasure. Scientific discourse has drifted to other meanings and there is no consensus on a single definition. Emotion is often intertwined with mood, temperament, personality, disposition, and motivation.

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Research suggests that interaction between humans and digital environments characterizes a form of companionship in addition to technical convenience. To this effect, humans have attempted to design computer systems able to demonstrably empathize with the human affective experience. Facial electromyography EMG is one such technique enabling machines to access to human affective states.

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Dynamic Emotional Communication View all 22 Articles. Researchers have theoretically proposed that humans decode other individuals' emotions or elementary cognitive appraisals from particular sets of facial action units AUs. Furthermore, the previous studies relied on facial expressions of actors and no study used spontaneous and dynamic facial expressions in naturalistic settings.

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Affective and human-centered computing have attracted a lot of attention during the past years, mainly due to the abundance of devices and environments able to exploit multimodal input from the part of the users and adapt their functionality to their preferences or individual habits. In this paper, we describe a multi-cue, dynamic approach to detect emotion in naturalistic video sequences. Contrary to strictly controlled recording conditions of audiovisual material, the proposed approach focuses on sequences taken from nearly real world situations. The algorithm is deployed on an audiovisual database which was recorded simulating human-human discourse and, therefore, contains less extreme expressivity and subtle variations of a number of emotion labels.

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