Keisuke Suzuki

Toward Computational Phenomenology

Biography:

Keisuke Suzuki obtained his Ph.D degree on the subject of artificial life from the University of Tokyo in 2007. He stayed as a research fellow in RIKEN Brain Science Institute, where he carried out research into human cognitive functions in virtual reality environments (2008-2011). After leaving Japan, he worked as a research fellow at the Sackler Centre for Consciousness Science in University of Sussex, UK (2011-2021), where he developed various virtual reality platforms for the experimental study of embodied self-consciousness. In 2021, he joined the Center for Human Nature, Artificial Intelligence, and Neuroscience (CHAIN) in Hokkaido University, Japan as a specially appointed lecturer to continue his work in embodied cognition and conscious presence. One of Keisuke's key research focuses is about conscious presence; i.e. the subjective feeling of being "here and now". The sense of presence is one of the important aspects of our subjective conscious experience, but its underlying neural mechanisms remain poorly understood. His approach builds on state-of-the-art virtual reality to experimentally manipulate the bodily and mental states, which is complimented by theoretical modeling.

Dong Seog HAN

Recent Advances in Understanding Human Emotion through AI

Advancements in artificial intelligence (AI) have significantly improved our understanding of human emotions. Emotion recognition is crucial in healthcare, customer service, and human-computer interaction, offering personalized and practical solutions. This talk will provide an overview of the importance and applications of emotion recognition, setting the stage for an in-depth exploration of the latest neural network architectures. We will examine convolutional neural networks (CNNs), VGGs, Inception, ResNet, and Xception, highlighting their roles in improving emotion recognition accuracy. These advancements bring us closer to reliable emotion detection despite challenges such as irrelevant facial images, diverse backgrounds, and varying image sizes.

Next, we will discuss the facial image threshing (FIT) machine, which was developed to enhance dataset quality by removing irrelevant features and standardizing images. Building on this, we will examine facial landmark-based emotion recognition, which uses deep learning to detect crucial facial points, and context-based emotion recognition, which incorporates contextual information for robust accuracy. Context-based models are particularly significant as they consider environmental and situational factors, leading to a more comprehensive understanding of emotional expressions. The effectiveness of these models, evaluated using datasets like FER2013 and EMOTIC, demonstrates significant improvements in handling diverse emotional expressions.

Overall, this talk will provide a deep understanding of the current state and potential of AI-driven emotion recognition, focusing on its transformative impact on the development of more intelligent systems.

Biography:

Dong Seog Han (Senior Member, IEEE) received his B.S. degree in Electronic Engineering from Kyungpook National University (KNU), Daegu, South Korea, in 1987, and the M.S. and Ph.D. degrees in Electrical Engineering from Korea Advanced Institute of Science and Technology, Daejeon, South Korea, in 1989 and 1993, respectively. From 1987 to 1996, he was with Samsung Electronics Company Ltd., where he developed the transmission systems for HDTV receivers. Since 1996, he has been a professor at the School of Electronics Engineering, KNU, as a professor. In 2004, he was a Courtesy Associate Professor in the Department of Electrical and Computer Engineering at the University of Florida. He was the Director of the Center of Digital TV and Broadcasting at the Institute for Information Technology Advancement from 2006 to 2008. He has been the Director of the Center for ICT and Automotive Convergence, KNU, since 2011. His research interests include intelligent signal processing and autonomous vehicles.