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While machine learning approaches to visual emotion recognition offer great promise, current methods consider training and testing models on small scale datasets covering limited visual emotion concepts. Our analysis identifies an important…
Automatic emotion recognition is one of the central concerns of the Human-Computer Interaction field as it can bridge the gap between humans and machines. Current works train deep learning models on low-level data representations to solve…
Practical Imitation Learning (IL) systems rely on large human demonstration datasets for successful policy learning. However, challenges lie in maintaining the quality of collected data and addressing the suboptimal nature of some…
Technology has become an essential part in every aspect of our lives. However the key to a successful implementation of a technology depends on the acceptance by the general public. In order to increase the acceptance various approaches can…
Deep fakes became extremely popular in the last years, also thanks to their increasing realism. Therefore, there is the need to measures human's ability to distinguish between real and synthetic face images when confronted with cutting-edge…
The perceptual representations supporting our ability to recognize faces remain a computational mystery. Deep neural networks offer mechanistic hypotheses for human face perception, but theoretically distinct models often make…
With recent advances in speech synthesis, synthetic data is becoming a viable alternative to real data for training speech recognition models. However, machine learning with synthetic data is not trivial due to the gap between the synthetic…
Facial expressions are vital in human communication and significantly influence outcomes in human-robot interaction (HRI), such as likeability, trust, and companionship. However, current methods for generating robotic facial expressions are…
Facial Emotion Recognition (FER) plays a crucial role in computer vision, with significant applications in human-computer interaction, affective computing, and areas such as mental health monitoring and personalized learning environments.…
Emotion recognition and generation have emerged as crucial topics in Artificial Intelligence research, playing a significant role in enhancing human-computer interaction within healthcare, customer service, and other fields. Although…
Human-robot interactions (HRI) can be modeled as dynamic or differential games with incomplete information, where each agent holds private reward parameters. Due to the open challenge in finding perfect Bayesian equilibria of such games,…
Robots operating alongside humans often encounter unfamiliar environments that make autonomous task completion challenging. Though improving models and increasing dataset size can enhance a robot's performance in unseen environments, data…
A growing number of approaches exist to generate explanations for image classification. However, few of these approaches are subjected to human-subject evaluations, partly because it is challenging to design controlled experiments with…
The goal of the Interactive Learning for Artificial Intelligence (AI) for Human-Robot Interaction (HRI) symposium is to bring together the large community of researchers working on interactive learning scenarios for interactive robotics.…
Recent advances in machine learning and computer vision have led to reported facial recognition accuracies surpassing human performance. We question if these systems will translate to real-world forensic scenarios in which a potentially…
To better interact with users, a social robot should understand the users' behavior, infer the intention, and respond appropriately. Machine learning is one way of implementing robot intelligence. It provides the ability to automatically…
The use of large-scale, web-scraped datasets to train face recognition models has raised significant privacy and bias concerns. Synthetic methods mitigate these concerns and provide scalable and controllable face generation to enable fair…
A unique challenge in creating high-quality animatable and relightable 3D avatars of people is modeling human eyes. The challenge of synthesizing eyes is multifold as it requires 1) appropriate representations for the various components of…
Affective computing stands at the forefront of artificial intelligence (AI), seeking to imbue machines with the ability to comprehend and respond to human emotions. Central to this field is emotion recognition, which endeavors to identify…
Humans have long been recorded in a variety of forms since antiquity. For example, sculptures and paintings were the primary media for depicting human beings before the invention of cameras. However, most current human-centric computer…