  Recognizing people by faces and other biometrics has been extensively studied
in computer vision. But these techniques do not work for identifying the wearer
of an egocentric (first-person) camera because that person rarely (if ever)
appears in their own first-person view. But while one's own face is not
frequently visible, their hands are: in fact, hands are among the most common
objects in one's own field of view. It is thus natural to ask whether the
appearance and motion patterns of people's hands are distinctive enough to
recognize them. In this paper, we systematically study the possibility of
Egocentric Hand Identification (EHI) with unconstrained egocentric hand
gestures. We explore several different visual cues, including color, shape,
skin texture, and depth maps to identify users' hands. Extensive ablation
experiments are conducted to analyze the properties of hands that are most
distinctive. Finally, we show that EHI can improve generalization of other
tasks, such as gesture recognition, by training adversarially to encourage
these models to ignore differences between users.
