Towards Open-set Gesture Recognition via Feature Activation Enhancement
  and Orthogonal Prototype Learning
Chen Liu Can Han Chengfeng Zhou Crystal Cai Suncheng Xiang Hualiang Ni Dahong Qian
Gesture recognition is a foundational task in human-machine interaction
(HMI). While there has been significant progress in gesture recognition based
on surface electromyography (sEMG), accurate recognition of predefined gestures
only within a closed set is still inadequate in practice. It is essential to
effectively discern and reject unknown gestures of disinterest in a robust
system. Numerous methods based on prototype learning (PL) have been proposed to
tackle this open set recognition (OSR) problem. However, they do not fully
explore the inherent distinctions between known and unknown classes. In this
paper, we propose a more effective PL method leveraging two novel and inherent
distinctions, feature activation level and projection inconsistency.
Specifically, the Feature Activation Enhancement Mechanism (FAEM) widens the
gap in feature activation values between known and unknown classes.
Furthermore, we introduce Orthogonal Prototype Learning (OPL) to construct
multiple perspectives. OPL acts to project a sample from orthogonal directions
to maximize the distinction between its two projections, where unknown samples
will be projected near the clusters of different known classes while known
samples still maintain intra-class similarity. Our proposed method
simultaneously achieves accurate closed-set classification for predefined
gestures and effective rejection for unknown gestures. Extensive experiments
demonstrate its efficacy and superiority in open-set gesture recognition based
on sEMG.