Isabelle Hupont

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An effective method for the automatic classification of facial expressions into emotional categories is presented. The system is able to classify the user facial expression in terms of the six Ekman’s universal emotions (plus the neutral one), giving a membership confidence value to each emotional category. The method is capable of analysing any subject,(More)
User emotion detection is a very useful input to develop affective computing strategies in modern human computer interaction. In this paper, an effective system for facial emotional classification is described. The main distinguishing feature of our work is that the system does not simply provide a classification in terms of a set of discrete emotional(More)
Human computer intelligent interaction is an emerging field aimed at providing natural ways for humans to use computers as aids. It is argued that for a computer to be able to interact with humans it needs to have the communication skills of humans. One of these skills is the affective aspect of communication, which is recognized to be a crucial part of(More)
This paper proposes an effective system for continuous facial affect recognition from videos. The system operates in a continuous 2D emotional space, characterized by evaluation and activation factors. It makes use, for each video frame, of a classification method able to output the exact location (2D point coordinates) of a still facial image in that(More)
The recognition of emotional information is a key step toward giving computers the ability to interact more naturally and intelligently with people. This paper presents a completely automated real-time system for facial expression’s recognition based on facial features’ tracking and a simple emotional classification method. Facial features’ tracking uses a(More)
The capability of perceiving and expressing emotions through different modalities is a key issue for the enhancement of human-computer interaction. In this paper we present a novel architecture for the development of intelligent multimodal affective interfaces. It is based on the integration of Sentic Computing, a new opinion mining and sentiment analysis(More)
Affective content annotations are typically acquired from subjective manual assessments by experts in supervised laboratory tests. While well manageable, such campaigns are expensive, time-consuming and results may not be generalizable to larger audiences. Crowdsourcing constitutes a promising approach for quickly collecting data with wide demographic scope(More)
We present a simple and computationally feasible method to perform automatic emotional classification of facial expressions. We propose the use of 10 characteristic points (that are part of the MPEG4 feature points) to extract relevant emotional information (basically five distances, presence of wrinkles and mouth shape). The method defines and detects the(More)
The interpretation of user facial expressions is a very useful method for emotional sensing and it constitutes an indispensable part of affective Human Computer Interface designs. Facial expressions are often classified into one of several basic emotion categories. This categorical approach seems poor to treat faces with blended emotions, as well as to(More)