  Hand-over-face gestures can provide important implicit interactions during
conversations, such as frustration or excitement. However, in situations where
interlocutors are not visible, such as phone calls or textual communication,
the potential meaning contained in the hand-over-face gestures is lost. In this
work, we present iFace, an unobtrusive, wearable impedance-sensing solution for
recognizing different hand-over-face gestures. In contrast to most existing
works, iFace does not require the placement of sensors on the user's face or
hands. Instead, we proposed a novel sensing configuration, the shoulders, which
remains invisible to both the user and outside observers. The system can
monitor the shoulder-to-shoulder impedance variation caused by gestures through
electrodes attached to each shoulder. We evaluated iFace in a user study with
eight participants, collecting six kinds of hand-over-face gestures with
different meanings. Using a convolutional neural network and a user-dependent
classification, iFace reaches 82.58 \% macro F1 score. We discuss potential
application scenarios of iFace as an implicit interaction interface.
