  Wearable cameras allow people to record their daily activities from a
user-centered (First Person Vision) perspective. Due to their favorable
location, wearable cameras frequently capture the hands of the user, and may
thus represent a promising user-machine interaction tool for different
applications. Existent First Person Vision methods handle hand segmentation as
a background-foreground problem, ignoring two important facts: i) hands are not
a single "skin-like" moving element, but a pair of interacting cooperative
entities, ii) close hand interactions may lead to hand-to-hand occlusions and,
as a consequence, create a single hand-like segment. These facts complicate a
proper understanding of hand movements and interactions. Our approach extends
traditional background-foreground strategies, by including a
hand-identification step (left-right) based on a Maxwell distribution of angle
and position. Hand-to-hand occlusions are addressed by exploiting temporal
superpixels. The experimental results show that, in addition to a reliable
left/right hand-segmentation, our approach considerably improves the
traditional background-foreground hand-segmentation.
