DKE research theme

Affective & Visual Computing Lab (AVCL)

How can a machine interpret human behavior as accurately as possible? And how can human behavior be used to personalize the way a machine works? Research within AVCL aims to enable the automated sensing of behaviors, emotions, and intents to improve people’s daily lives. AVCL makes use of the latest advances in Computer Vision, Natural Language Processing and Artificial Intelligence.

Research focus and application

The Affective & Visual Computing Lab builds techniques that allow machines to combine data from different sources and interpret human behavior as accurately as possible. The scope of the lab encompasses both fundamental research and research into a wide range of innovative applications.

AVCL researchers are currently working in the domains of multimodal emotion and personality recognition in the wild (e.g. educational settings and beyond), activity recognition for senior citizens (using computer vision, health records, digital interactions, ambient sensors), knowledge transfer in affective computing, (visual) event recognition and text retrieval.

AVCL projects are tested in real operational environments including schools, hospitals, daily care centers and home environments.

Highlighted publications

  • Ghaleb, E., Popa, M., & Asteriadis, S. (2019). Multimodal and Temporal Perception of Audio-visual Cues for Emotion Recognition. In 8th International Conference on Affective Computing & Intelligent Interaction (ACII 2019), Cambridge, United Kingdom
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  • Karkazis, P., Leligou, H. C., Trakadas, P., Vretos, N., Asteriadis, S., Daras, P., & Standen, P. (2019). Technologies Facilitating Smart Pedagogy. In Didactics of Smart Pedagogy (pp. 433-451). Springer.
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  • Briassouli, A. (2018). Unknown Crowd Event Detection from Phase-Based Statistics. In AVSS 2018: 15th IEEE International Conference on Advanced Video and Signal-based Surveillance
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  • Alvarez, F., Popa, M., Solachidis, V., Hernandez-Penaloza, G., Belmonte-Hernandez, A., Asteriadis, S., Vretos, N., Quintana, M., Theodoridis, T., Dotti, D., & Daras, P. (2018). Behavior Analysis through Multimodal Sensing for Care of Parkinson's and Alzheimer's Patients. Ieee Multimedia, 25(1), 14-25.
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  • Dotti, D., Popa, M., & Asteriadis, S. (2018). Behavior and Personality Analysis in a nonsocial context Dataset. In 2018 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
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  • Maggiolo, M., Spanakis, G. Autoregressive Convolutional Recurrent Neural Network for Univariate and Multivariate Time Series Prediction, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2019), Brugges, Belgium, 24-26 April 2019

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