The papers in this special section focus on commercial applications for affective computing. One of the main goals of affective computing is to create machines which can adapt to users' emotions in order to produce more natural and efficient interaction. Emotion recognition is thus a central component of the field, and is based on a variety of measurements (facial expressions, speech, gait patterns, physiology, eye tracking, etc.) that are analyzed using advanced pattern recognition techniques. Furthermore, researchers and entrepreneurs have identified countless possible applications of affect-aware technology, from health and driver monitoring to exercise [6] and computer game adaptation. However, although great scientific advances have been made and many applications have been proposed, only few robust implementations have been presented or validated, and commercial adoption of affect-aware technology has been marginal. One rare example of a successful application is the smile detector used in digital cameras to automatically take pictures when the subject is smiling. This weak adoption of the technology can be attributed to several unsolved challenges in the domain of affective computing.