When people talk, they tend to adopt the behaviors, gestures, and language of their conversational partners. This "accommodation" to one's partners is largely automatic, but the degree to which it occurs is influenced by social factors, such as gender, relative power, and attraction. In settings where such social information is not known, this accommodation can be a useful cue for the missing information. This is especially important in web-based communication, where social dynamics are often fluid and rarely stated explicitly. But connecting accommodation and social dynamics on the web requires accurate quantification of the different amounts of accommodation being made.

The ability to quickly compute hand geometry measurements from a freely posed hand offers advantages to biometric identification systems. While hand geometry systems are not new, typical measurements of lengths and widths of fingers and palms require rigid placement of the hand against pegs. Slight deviations in hand position, finger stretch or pressure can yield different measurements. This paper offers novel approaches to computing hand geometry measurements from frontal views of freely posed hands. These approaches offer advantages in hygiene, comfort and reliability. Our algorithms segment the hand from a known background under spot lights and locate feature points along the fingers and wrists. Given a database of 54 hand images, with three different images of the same hand of each subject, our approach uniquely identified a previously unseen hand with an overall accuracy of 92%.

The problem investigated in this paper is the quick identification of free-form 2D objects using a coefficient based indexing technique. We demonstrate that combining implicit polynomial shape descriptors with calibration techniques has the potential to quickly identify images of similar shapes. As a pilot experiment, our approach is evaluated against a small database consisting of 15 images, with three different images from five categories. The index system achieves an overall top-3 matching accuracy of 88.9% comparing to 91.1% of the traditional brutal force method on the same database with a computational cutoff of 67.7%.