Peter W. McOwan

TL;DR: This paper empirically evaluates facial representation based on statistical local features, Local Binary Patterns, for person-independent facial expression recognition, and observes that LBP features perform stably and robustly over a useful range of low resolutions of face images, and yield promising performance in compressed low-resolution video sequences captured in real-world environments.

TL;DR: A novel low-computation discriminative feature space is introduced for facial expression recognition capable of robust performance over a rang of image resolutions based on the simple local binary patterns (LBP) for representing salient micro-patterns of face images.

TL;DR: A fully automated, multistage system for real-time recognition of facial expression that is able to operate effectively in cluttered and dynamic scenes, recognizing the six emotions universally associated with unique facial expressions, namely happiness, sadness, disgust, surprise, fear, and anger.

TL;DR: An initial evaluation suggests that patterns of postural behaviour can be used to accurately predict the engagement of the children with the robot, thus making the approach suitable for integration into an affect recognition system for a game companion in a real world scenario.

TL;DR: This paper addresses the problem of how to reliably detect motion in first-order and some second-order motion stimuli by computed as the space-time direction in which the difference in image illuminance from the local mean is conserved.