A Mimetic Strategy to Engage Voluntary Physical Activity In Interactive Entertainment

We describe the design and implementation of a vision based interactive entertainment system that makes use of both involuntary and voluntary control paradigms. Unintentional input to the system from a potential viewer is used to drive attention-getting output and encourage the transition to voluntary interactive behaviour. The iMime system consists of a character animation engine based on the interaction metaphor of a mime performer that simulates non-verbal communication strategies, without spoken dialogue, to capture and hold the attention of a viewer. The system was developed in the context of a project studying care of dementia sufferers. Care for a dementia sufferer can place unreasonable demands on the time and attentional resources of their caregivers or family members. Our study contributes to the eventual development of a system aimed at providing relief to dementia caregivers, while at the same time serving as a source of pleasant interactive entertainment for viewers. The work reported here is also aimed at a more general study of the design of interactive entertainment systems involving a mixture of voluntary and involuntary control.Comment: 6 pages, 7 figures, ECAG08 worksho

Efficient smile detection by Extreme Learning Machine

Smile detection is a specialized task in facial expression analysis with applications such as photo selection, user experience analysis, and patient monitoring. As one of the most important and informative expressions, smile conveys the underlying emotion status such as joy, happiness, and satisfaction. In this paper, an efficient smile detection approach is proposed based on Extreme Learning Machine (ELM). The faces are first detected and a holistic flow-based face registration is applied which does not need any manual labeling or key point detection. Then ELM is used to train the classifier. The proposed smile detector is tested with different feature descriptors on publicly available databases including real-world face images. The comparisons against benchmark classifiers including Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) suggest that the proposed ELM based smile detector in general performs better and is very efficient. Compared to state-of-the-art smile detector, the proposed method achieves competitive results without preprocessing and manual registration

What does touch tell us about emotions in touchscreen-based gameplay?

This is the post-print version of the Article. The official published version can be accessed from the link below - Copyright @ 2012 ACM. It is posted here by permission of ACM for your personal use. Not for redistribution.Nowadays, more and more people play games on touch-screen mobile phones. This phenomenon raises a very interesting question: does touch behaviour reflect the player’s emotional state? If possible, this would not only be a valuable evaluation indicator for game designers, but also for real-time personalization of the game experience. Psychology studies on acted touch behaviour show the existence of discriminative affective profiles. In this paper, finger-stroke features during gameplay on an iPod were extracted and their discriminative power analysed. Based on touch-behaviour, machine learning algorithms were used to build systems for automatically discriminating between four emotional states (Excited, Relaxed, Frustrated, Bored), two levels of arousal and two levels of valence. The results were very interesting reaching between 69% and 77% of correct discrimination between the four emotional states. Higher results (~89%) were obtained for discriminating between two levels of arousal and two levels of valence

Facial Emotion Recognition in VR Games

Emotion detection is a crucial component of Games User Research (GUR), as it allows game developers to gain insights into players' emotional experiences and tailor their games accordingly. However, detecting emotions in Virtual Reality (VR) games is challenging due to the Head-Mounted Display (HMD) that covers the top part of the player's face, namely, their eyes and eyebrows, which provide crucial information for recognizing the impression. To tackle this we used a Convolutional Neural Network (CNN) to train a model to predict emotions in full-face images where the eyes and eyebrows are covered. We used the FER2013 dataset, which we modified to cover eyes and eyebrows in images. The model in these images can accurately recognize seven different emotions which are anger, happiness, disgust, fear, impartiality, sadness and surprise. We assessed the model's performance by testing it on two VR games and using it to detect players' emotions. We collected self-reported emotion data from the players after the gameplay sessions. We analyzed the data collected from our experiment to understand which emotions players experience during the gameplay. We found that our approach has the potential to enhance gameplay analysis by enabling the detection of players' emotions in VR games, which can help game developers create more engaging and immersive game experiences.Comment: IEEE Conference on Games 202

Does your profile say it all? Using demographics to predict expressive head movement during gameplay

In this work, we explore the relation between expressive head movement and user profile information in game play settings. Facial gesture analysis cues are statistically correlated with players' demographic characteristics in two different settings, during game-play and at events of special interest (when the player loses during game play). Experiments were conducted on the Siren database, which consists of 58 participants, playing a modified version of the Super Mario. Here, as player demographics are considered the gender and age, while the statistical importance of certain facial cues (other than typical/universal facial expressions) was analyzed. The proposed analysis aims at exploring the option of utilizing demographic characteristics as part of users' profiling scheme and interpreting visual behavior in a manner that takes into account those features.peer-reviewe