Towards Open-set Gesture Recognition via Feature Activation Enhancement and Orthogonal Prototype Learning

TL;DR

This paper introduces a novel open-set gesture recognition method using feature activation enhancement and orthogonal prototype learning, significantly improving the ability to distinguish known from unknown gestures in sEMG-based systems.

Contribution

It proposes two innovative techniques, FAEM and OPL, to better differentiate known and unknown gestures, advancing open-set recognition in gesture-based human-machine interaction.

Findings

Enhanced recognition accuracy for known gestures.

Improved rejection of unknown gestures.

Outperforms existing methods in extensive experiments.

Abstract

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.