A Supervised Approach to Support the Analysis and the Classification of Non Verbal Humans Communications

Bevilacqua, Vitoantonio; Suma, Marco; Mandolino, Giovanni; Caccia, Michele; Tucci, Simone; Tommaso, Emanuela De; Marra, Giuseppe

doi:10.1007/978-3-642-24728-6_58

Cited by 2 publications

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“…Spontaneous body gestures, facial expressions and voices could reveal the real emotional state of a subject. The work presented in this paper is closely related to [8,11] and [9] in which are described some applications of emotion recognition. All domains together (facial, gestural, and vocal) are a solid base for multimodal emotion recognition and represent our final goal.…”

Section: Multimodal Emotion Recognitionmentioning

confidence: 97%

“…In addition, the ability to recognize emotions and mood from facial expressions [8][9][10], human voice [11] and gestures [5][6][7]12] is an important task for the future of human-computer interaction. One of our goals is to introduce a new way to measure the level of resonance and empathy [13] between two people, for example during a job interview when a psychologist measures the level of anxiety, stress and much more using formal schemes and evaluating habits of the job applicant.…”

Section: Introductionmentioning

confidence: 99%

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A new tool for gestural action recognition to support decisions in emotional framework

Bevilacqua

Barone

Cipriani

et al. 2014

2014 IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA) Proceedings

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Introduction and objective: the purpose of this work is to design and implement an innovative tool to recognize 16 different human gestural actions and use them to predict 7 different emotional states. The solution proposed in this paper is based on RGB and depth information of 2D/3D images acquired from a commercial RGB-D sensor called Kinect. Materials: the dataset is a collection of several human actions made by different actors. Each action is performed by each actor for three times in each video. 20 actors perform 16 different actions, both seated and upright, totalling 40 videos per actor. Methods: human gestural actions are recognized by means feature extractions as angles and distances related to joints of human skeleton from RGB and depth images. Emotions are selected according to the state-of-the-art.Experimental results: despite truly similar actions, the overallaccuracy reached is approximately 80%. Conclusions and future works: the proposed work seems to be back-ground-and speedindependent, and it will be used in the future as part of a multimodal emotion recognition software based on facial expressions and speech analysis as well.

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Section: Multimodal Emotion Recognitionmentioning

confidence: 97%