Low personality-sensitive feature learning for radar-based gesture recognition

Abstract

Radar-based sensing of gestures has gained tremendous attention with the recent advancements in radar technologies. However, evident discrepancies exist in the gesture samples due to hand flexibility and individual habits. It is challenging for traditional methods to identify the gestures from unknown data sources. Cross-person (Cross-scenario) recognition refers to a recognition where the training and test samples are from different people (scenarios), respectively. To explore how the recognition performance is affected by the individual habits, the reasons are analyzed and visualized through the experiments. On this basis, HandNet is targeted proposed for the low personality-sensitive feature learning and it has two main contributions. First, a Stepped Data Augmentation (SDA) is proposed to reduce the sample interferences by non-coherent accumulating, and capture the inter-frame dependencies. Second, a Focus on Generalization loss (FoG loss) is proposed to highlight the generalized feature learning by res tricting the distances of inter-source features. Extensive experiments demonstrate that HandNet effectively reduces the classifier’s sensitivity to the personalized habits, and outperforms the existing state-of-the-art methods on the cross-person and cross-scenario gesture recognition. To the best of our knowledge, it is the first time to dedicate to addressing the radar-based gesture recognition with low personal sensitivity, which is more suitable for practical scenarios.